Umm Al-Qura University

Umm Al-Qura University

CIS - Master Thesis


- 2020/07/21

CIS - Master Thesis

Computer Science and Engineering (SE)

Developing Wireless Surveillance Camera Forensic Tool using Software Engineering Methodology

Thesis ID: 1400-SET-2017F-01
Year: 2017
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Reem Muhammad Alshalawi

Keywords:

Illegal access, IP camera, Wireless Network, Wireshark, Development, Privacy

Abstract:

Software engineering is not only a practical science but also a way of thinking. In the context of a high demand for smart technology such as surveillance camera, software engineering can provide viable solutions; however, most people hesitate to use such technology because they are afraid their privacy would be threatened. In this research, we present a new tool in network forensic. Network forensic is a rapidly developed domain. We investigate the possible illegal access (attack) on wireless surveillance camera. This proposed tool is designed in two stages: (1) building a new monitoring scheme to maintain data privacy; (2) facilitating the investigation process that plays a big role for saving users’ privacy and keeping highly secure the places that use surveillance camera. To evaluate the performance, for the tool design, we used confusion matrix. This method provided recall values of 100% in two experiments: (1) Compared Source IP (CSIP) and (2) Camera Transmit Time Calculating (CTTC). Precision and accuracy achieved 100% and 99% in CSIP and CTTC respectively.

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Exploring the Characteristics of Business Process Modeling Solutions in the Saudi Market

Thesis ID: 1400-SET-2017F-02
Year: 2017
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Ameenah Hesham Abdulqader Naytah

Keywords:

Business Process Modeling (BPM), Business Process (PB), Supporting Tool, BPM solutions

Abstract:

Business Process Modeling (BPM) is a mechanism that separates all business aspects from the underlying technological and implementation aspects of a system to capture an organization’s processes and achieve their business objectives. Currently, there are many solutions for Business Process Modeling and Design offered by vendors, but the selection of one solution or another by customers usually done in an ad-hoc manner. There is no standard methodology can help in the selection of the most appropriate solution, considering the underlying environment that a customer might have and their limitations which might be incompatible with the provided BPM solutions. Therefore, the study aims to provide a useful supporting tool that can help customers pick the most appropriate BPM solutions for their organizations considering the architectural, functional, and usability requirements of their environments.

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An Optimized Technique for Securing and Hiding Data in Personal Computers

Thesis ID: 1400-SET-2017F-03
Year: 2017
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Nouf Awad Aljuaid

Keywords:

cryptography, sensitive data, security for personal computer, steganography

Abstract:

Usually, we need to secure sensitive data that we store on personal computers (PC) such as e-mail messages, credit card information, and corporate data. To accomplish this, we need to find appropriate techniques. In this work, a flexible security system suitable to hide sensitive text data on PC is proposed and implemented. The system hiding techniques involve cryptography and steganography as two layers to insure high security. In the first layer, two cryptographic algorithms are used; namely, AES and RSA encryption algorithms. The second layer is a steganography layer, which hides the encrypted data on image, audio, or video covers. The system uses cover-based steganography. In addition, it provides security information to the user to select the cover within the PC based on his/her security priority. The results revealed that the optimal cover if the user needs a huge capacity is video cover and if the user needs small size file with acceptable capacity the optimal cover is image cover. In addition, from the experimental results, we found that any hidden text will change no more thatn 50% of the length of the text on the media cover.

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Energy Optimization Techniques In Mobile Cloud Computing

Thesis ID: 1400-SET-2017F-04
Year: 2017
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Khadijah Salim Bahwaireth

Keywords:

N/A

Abstract:

Smart mobile devices became an essential component of our daily life around the globe. The limited processing capacity and battery lifetime of mobile devices are considered as challenges, especially when executing heavy applications. In this research, the cooperative cloudlet model is proposed to overcome these limitations. The cloudlets in this model cooperate with each other to fulfill users requests via Wi-Fi connections. There is no need to access the enterprise cloud through expensive technologies such as 3G and 4G. In this thesis, we developed an android application in mobile devices to offload the heavy tasks to the cooperative cloudlet model. we also, present an experimental results of the cooperative cloudlet model in Mobile Cloud Computing which enables computationally intensive tasks such as data mining, multimedia processing, and optical character recognition (OCR) applications to be offloaded from mobile device to the nearest cloudlet. In the proposed model, the cloudlets cooperate with each other to find the requested service by a user, and the results are returned back to the mobile device. To demonstrate the benefits of this approach, the cooperative cloudlet system execute the heavy computations of selected applications such as OCR android application and sorting huge arrays. We use MccSim simulator to test the proposed model. The evaluation of real experiments and simulation results show that mobile devices can benefit from offloading with around 70 % energy savings and significant performance gains compared to local execution at mobile devices.

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AN OPTIMAL PSEUDO-RANDOM NUMBER GENERATOR

Thesis ID: 1400-SET-2017M-01
Year: 2017
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Mohammad Sabir Jan Hafiz Akhoond

Keywords:

N/A

Abstract:

Pseudo-Random Number Generator PRNG is one of the basic blocks of information security, especially in Cryptography. PRNGs play a fundamental role in many other fields of computer science, such as Simulations, Testing and others. Many PRNGs have been proposed in the literature thus far. Only few PRNGs pass all randomness statistical tests. NIST 800-22 Test Suites for Random Numbers is one of the famous randomness statistical tests, which is used for approving PRNGs. In this thesis, we introduce a new PRNG, which is assumed to be optimal and hence called hear thereafter “Optimal Pseudo-Random Number Generator (OPRNG)”. The main idea is based on representing the output numbers as Finite State Machines (FSM) in order have normal distribution. In OPRNG, two buffers are used to provide randomness. The first buffer contains the list of numbers (states) that are used as a source for the random controller. The other buffer, on the other hand, is used to store the numbers that have been selected randomly from the first buffer. Accordingly, the whole contents of the first buffer will be stored in the second buffer randomly depending on the size of the buffers. Then, OPRNG outputs the random numbers whenever a number (state) is selected from the first buffer. The OPRNG does not repeat any state till the content of the first buffer is empty; hence OPRNG is optimal. The randomness results of the OPRNG, according to NIST tests, show that the proposed OPRNG is very attractive for applications that require RNG.

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Smart Phones Protection and Security Versus Power Consumption: Study and Analysis

Thesis ID: 1400-SET-2018F-01
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Esraa Mohammad Abdullah Ahmadoh

Keywords:

N/A

Abstract:

Obviously, our life is becoming more dependent on using smart phones to conduct variety of useful tasks. There are different mobile applications in many aspects of life including education, health, and transportation, and many others. As a recent example, these smart devices are being used increasingly in the health sector to facilitate the medical processes. Besides all these benefits of using smart phones, there are some critical challenges that limit their optimal usage. Among the main challenges facing the smart phone is the short battery lifetime due to power consumption especially when running applications with intensive computations. Energy drain from these phones is a limitation and it will even multiply during the next few years. That’s true charging up a smart phones require a negligible amount of power, but it is also true that the heavy energy consumption comes from how those devices are used once they are fully charged. Consider the fact that watching just one hour of video on a tablet or a smart phone every week consumes more electricity than two new Energy Star-qualified refrigerators can consume in an entire year. From here comes the motivation to study the power consumption of the smart devices, and mainly smart phones. On the other hand, most of the usage of these devices is done while they are connected to the internet through the known techniques such as Wi-Fi and 3G. This fact imposes the need for more protection to secure the private data of the users (that might include personal data or even financial transactions). In this research, we will measure and analyze the amount of power consumed by different components of the smart phones (Galaxy Note 4, Galaxy Beam, and iPhone 4) in order to characterize the total power consumption. Moreover, we will investigate the security concerns and threats related to the use of smart phones, and study the impact of the increasing need for more security on the power consumed by the smart phones especially when those devices are connected to the internet via different wireless technologies.

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Feasibility Study for Mobile Cloud-based Healthcare Systems in Saudi Arabia

Thesis ID: 1400-SET-2018F-02
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Suhaila Habeeb Hanash AlZahrani

Keywords:

N/A

Abstract:

Healthcare is a very important sector in every nation’s life. During the last few years, the healthcare industry was developed rapidly affected by the advances in the Information and Communication Technology (ICT). ICT is facilitating converged healthcare systems which will allow the healthcare stakeholders to seamlessly organize their activities, provide personalized and preventive healthcare to citizens, and improve systems and operational efficiencies. Indeed, with the emerging concepts of urban developments such as smart cities, a coordinated healthcare approach can manage health for the whole society at city, country, or global levels. However, future healthcare systems will require more computations and reliable communications. Mobile computing will play essential role in future healthcare systems as the mobile devices will provide an interface between the patients and the healthcare professionals and organizations. However, limited battery lifetime, limited processing capabilities, and limited storage capacities limit the benefits that mobile computing can bring to healthcare and other sectors. The concept of Cloud Computing relies on a network-based resource sharing to increase resource availability and reduce the cost. Mobile Cloud Computing (MCC) is an emerging technology that converges mobile computing and cloud computing to overcome limitations of mobile devices as the processing and storage for intensive jobs are transferred to take place at the cloud, and the final results are returned back to the mobile device. However, traditional MCC designs has its own challenges including high latency and large transmission power consumption when accessing compute and storage resources over the network. The cloudlet concept that will be discussed in this work is believed to overcome some of these MCC limitations. In this proposal, we investigate the feasibility and requirements of future healthcare infrastructure for Saudi Arabia. For this purpose, a study to explore the development of the healthcare systems over the last decades along with the developments of the ICT technology is conducted. Moreover, we indicate the motivations for future converged mobile cloud-based healthcare systems in KSA and the associated challenges. We also introduce a layered design for future healthcare systems in KSA. Another main contribution of this research is proposing an efficient Hybrid cloudlet-based mobile cloud computing model to be used in healthcare systems-cloudlet layer. The proposed Hybrid MCC model is simulated using MCCSim tool with changing wide set of simulation parameters. The obtained results for the time delay and consumed power show that the proposed model overcomes previously proposed MCC models.

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UNDERWATER WIRELESS SENSOR NETWORKS SCHEDULING

Thesis ID: 1400-SET-2018F-03
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Abrar Salem Alhazmi

Keywords:

LEACH, UMOD_LEACH, UWSNs, Energy consumption

Abstract:

In recent years, Underwater Wireless Sensor Networks (UWSNs) have garnered attention worldwide due to their significant role in several application domains. However, the unique characteristics of underwater environments pose a challenge when designing routing protocols for UWSNs. In this context, several works in the literature have been proposed and developed that aim to minimize the power consumption of nodes and prolong their lifetime. Using clustering-based algorithms has proven to be an eective strategy to minimize the energy consumption, especially for routing tasks. One of the well-known clustering-based protocols is the Low Energy Algorithm Adaptive Clustering Hierarchy (LEACH) protocol. In this thesis, we posit a new approach that extends the LEACH clustering protocol. The proposed approach makes use of the localization concept, new transmission scheduling to enhance the LEACH protocol and adapt it to UWSNs environments. The concept of clustering with localization is used to minimize the energy consumption. Instead of using basic clustering concept as in LEACH, it has been modified and extended so that each cluster should be formed based on its locations with all clusters transmitting data in one hop to a centralized sink. The communication between the dierent members of the cluster is based on single-hop. The sensor nodes are considered in a two-dimensional (2D) space. The proposed solution named UMOD_LEACH was implemented using MATLAB. UMOD_LEACH outperformed LEACH, on average by more than 30% when the amount of transmitted data is more than 70% of the maximum while looks the same with small transmitted data. Simulation results show, also, that the proposed approach outperforms LEACH protocol in terms of lower propagation delay. Therefore, based on the results we confirm that our proposed solution UMOD_LEACH achieve the performance of UWSNs. As future works, we would move the architecture to be for 3D underwater sensor networks in which depth can be added.

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Betic Retinopathy Auto Detection Using Image Processing

Thesis ID: 1400-SET-2018F-04
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Malak Talaat Salem Bantan

Keywords:

N/A

Abstract:

Diabetic retinopathy (DR) is a common eye disease. Nowadays, around 1.7 million of the Saudi population has diabetic retinopathy. Diabetic Retinopathy is one of the consequences of Diabetes. DR can be detected through its stages with many features such as blood vessels, soft and hard exudates, microaneurysms, blot hemorrhages, etc. Automatic detection of these features will enhance the future of medicine. It may help physicians to make their decisions. In this thesis, two of the features are detected by using image processing, blood vessels, and exudates. The database used is the HRF of a total of 45 fundus images. MATLAB is used for image processing. Both features will pass through the preprocessing stages of image acquisition, a conversion to gray, a contrast enhancement, an intensity adjustment, as well as complementing and histogram equalization. They will then go through the image processing steps. For example, binarization, which is a morphological operation with different structuring elements that are selected from the characteristics and noise removal of each feature. The proposed method shows a specificity of 97%, a sensitivity of 69% and an accuracy of 94%.

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IOT SECURITY EVALUATION

Thesis ID: 1400-SET-2018F-05
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Norah Ali M. Alassaf

Keywords:

Internet of Things; Medical data; AES; SIMON cipher; SPECK cipher; Light weight cryptography.

Abstract:

IoT is a very hot research area which is supposed to change the outlook of modern world by the end of this decade. Short period monitoring and emergency notification of healthcare signals is becoming affordable with existence of internet of things (IoT) support. However, IoT does not prevent challenges that may hinder the appropriate safe spread of medical solutions. Confidentiality of data is vital making a real fear requesting cryptography. The limitations in memory, computations processing, power consumptions and small size devices contradict the robust encryption process asking for help of light-weight-cryptography to handle practically. This work presents a comparative analysis of performance evaluation of three trusted candidate encryption algorithms, namely AES, SPECK and SIMON, which are simulated and compared in details to distinguish which has the best behaviour to be nominated. The work presented in this thesis studies the performance of SIMON light-weight-cryptography algorithm for its possible use in an IoT driven setup. To achieve performance in practical prospective, the contribution looks into SIMON cipher’s characteristics considering utilizing it for internet of things (IoT) healthcare applications. The work suggests further improvement to implement the original SIMON cryptography in order to reduce the encryption time. Thus, The optimized SIMON has been compared to Advanced Encryption Standard (AES), SPECK and the original SIMON block cipher algorithms in terms of execution time, memory consumption. The results show that the proposed work is suitable for securing data in an IoT driven setup.

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Improving Cloud Computing Performance by Enhancing Task Scheduling Algorithms

Thesis ID: 1400-SET-2018F-06
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Tahani Abid Mohammed Aladwani

Keywords:

Task scheduling algorithms; Tasks Classification; Virtual Machines categories; Tasks importance; load balance; performance.

Abstract:

Cloud computing is one of the most important technologies used in recent times, it allows users (individuals and organizations) to access computing resources (software, hardware, and platform) as services remotely through the Internet. Cloud computing is distinguished from traditional computing paradigms by its scalability, adjustable costs, accessibility, reliability, and on-demand pay-as-you-go services). As cloud computing is serving millions of users simultaneously, it must have the ability to meet all users requests with high performance and guarantee of quality of service (QoS). Therefore, we need to implement an appropriate task scheduling algorithm to fairly and efficiently meet these requests. Task scheduling problem is the one of the most critical issues in cloud computing environment because cloud performance depends mainly on it. There are various types of scheduling algorithms; some of them are static scheduling algorithms that are considered suitable for small or medium scale cloud computing; and dynamic scheduling algorithms that are considered suitable for large scale cloud computing environments. In this research, we attempt to improve dynamic and static task scheduling performance by using four different proposed methods which are: first method is Tasks Classification and Virtual Machines Categorization (TCVC) based on Tasks lengths, second method is Task Scheduling Algorithms based on Least Load Virtual Machine (LLVM), third method is Tasks Scheduling Algorithms based on Tasks Classification and Least Load Virtual Machines (TCLL), and fourth method is TCVC based on tasks importance. In order to measure the performance achieved by these methods, they will be applied on a set of static task scheduling algorithms such as First Come First Service (FCFS), Shortest Job First (SJF), and MAX-MIN scheduling algorithms. The last method will be applied too on one dynamic tasks scheduling algorithms is Analytic Hierarchy Process (AHP) tasks scheduling algorithms. The cloudsim simulator has been used to measure their impact on algorithm complexity, resource availability, Total Execution Time (TET), Total Waiting Time (TWT), and Total Finish Time (TFT).

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Combined Detection and Classification Based on Deep Learning: With Application to Vehicle Classification

Thesis ID: 1400-SET-2018F-07
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Mariah Ibrahem Alshieck

Keywords:

N/A

Abstract:

Object detection and classification is a very popular field of research in computer vision. Despite the increasing number of object detection and classification studies, there is still a need to develop a more accurate and effective method, as it is a challenging field for researchers. Currently, there is no publicly available method that combines the detection and classification of vehicles at high accuracy. There are many factors that play an important role in increasing detection and classification accuracy, such as the selected features and learning methods. The deep learning approach has been widely used for the purposes of object detection and classification. In this study, an accurate and efficient system that combines vehicle detection and classification was developed using deep learning. This system is comprised of two major techniques. The first combines many detectors that were trained on both Haar-like and HOG features using the VJ algorithm. The aim of this process is to increase the number of true vehicle regions extracted from images. These regions are used to create the label images. The second is a deep learning technique, where the label images are used to train a neural network (CNN) to achieve very high accuracy in the detection and classification of vehicles images. Three types of experiments were conducted and examined within this thesis. The first was based on training the deep network learning to perform vehicle detection and classification tasks. The second and third experiments relied on the use of deep learning networks to extract the features of the vehicles followed by the classifiers (SVM or KNN). The experimental results showed that combining Haar and HOG detectors effectively increased the number of true vehicle regions extracted from images. The proposed system, which combined detection and classification tasks, provided an accuracy of 99.53%.

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Arabic Text Steganography Methods to Improve Capacity and Data Security

Thesis ID: 1400-SET-2018F-08
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Safia Meteb Awad Alnofaie

Keywords:

N/A

Abstract:

Steganography is a technique used for data security purpose. It is defined as a process of hiding the communication between two parties by embedding the secret data inside a carrier file to protect the data during its transmission. The carrier files for the confidential message are of different types including image, audio, video, and text. The text file is more challenging as compared to other files due to the lack of redundant bits. A lot of literature is available on Steganography techniques, using text as a cover file, for English language. However, the development of Steganography methods for Arabic language is still a challenging research area. Consequently, this thesis has presented two methods to improve the capacity and security of text Steganography, targeting Arabic language. The first method is based on the fact that the characters are connected to each other in the words in the Arabic text. Therefore, the first method aims to improve the capacity of the work using Kashida (extension character) and pseudo-space. While the second method uses only pseudo-spaces to hide the secret. Since the second method does not depend on a unique feature in Arabic, it can be applied to other languages of the world. Both proposed methods can also be used for languages similar to Arabic such as Urdu and Persian. Experimental results have shown that the proposed algorithms have achieved the high capacity ratio as compared to state-of-the-art Steganography methods for Arabic language. Furthermore, the proposed methods have also enhanced the security features.

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Toward a methodology for building hybrid multi-agent system with application to E-Learning

Thesis ID: 1400-SET-2018F-09
Year: 2018
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Fatimah Muhammad Abo Alnaja

Keywords:

N/A

Abstract:

One of the biggest challenges in education is teaching mathematics especially for children because that will effect on their progress in the future. Teaching mathematics for children is very important because it supportive for their curiosity nature. Some children gain low level when studying mathematics. The main reason for that performance is a misconception for basic concepts in mathematics. Using technologies play an important role in supporting mathematics learning and teaching. The goal of this research is to develop e-learning system for basic mathematics to resolve the misconception and lack of knowledge problem for the children at primary education and pre-school grade. The system builds from hybrid multi-agents, where each agent may be implemented using different type of intelligence. Fuzzy logic used to determine the agent responsible to take an action.

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Fingerprint and location based multifactor authentication for mobile applications

Thesis ID: 1400-SET-2019F-01
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Norah Abdullah AL-Dumiji

Keywords:

Multifactor; Authentication; Biometrics; location based; Smartphone; Privacy.

Abstract:

Authentication, which is the verification of identity, is one of the most important security features. It usually depends on three factors: something you know (knowledge), something you have (token) and something you are (biometrics). In this thesis, we propose the use of biometrics (fingerprints) with a fourth factor, namely location (i.e., where you are), in order to develop a privacyfriendly multi-factor authentication scheme suitable for smartphone applications.

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Efficient Resource Allocated for Time-Sensitive IoT Applications In Cloud And Fog Environment

Thesis ID: 1400-SET-2019F-02
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Lina Husain Kazem

Keywords:

N/A

Abstract:

In recent years, Internet of Things (IoT) has attracted the attention of both academia and commercial organizations. Nowadays, IoT provides great benefits to society that lead to significant improvements of the quality of our daily life. Examples include: smart city, smart homes, autonomous driving cars or airplanes ,and health monitoring systems. Cloud computing provides to IoT systems a series of services such as data computing, processing or storage, analysis and securing. It is estimated that by the year 2025, approximately trillion IoT devices will be used. As a result, a huge amount of data is going to be generated. In addition, in order to efficiently and accurately work, there are situations where IoT applications (such as Self Driving, Health Monitoring, etc.) require quick responses. In this context, the traditional Cloud computing systems will have difficulties in handling and providing services. To balance this scenario, and in order to reach this objective and to overcome the disadvantages of Cloud computing, a new computing model called Fog computing has been proposed. In this work, we first introduce a comparison between these two paradigms Fog computing and Cloud computing . We consider task scheduling for IoT applications in a Cloud - Fog computing environments, where the Fog computing does not eliminate the Cloud computing, it is complementary to it . For efficiently executing IoT applications. We are proposing an efficient architecture and algorithm for resource allocation in Fog computing environment. Our algorithm overcomes some problem on the requirement of service level flexibility and availability of resources whose major Target is to improve the Quality of Service (QoS) of IoT applications .In our work, we use CloudAnalyst, CloudSim and iFogSim simulation toolkit to simulate our systems. The numerical findings show that our system achieves better performance and work more efficient than Cloud computing. It also reduced the response time, processing time , networks usage in Cloud and cost of execution.

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Exploring the Integration of WSN with Cloud Computing

Thesis ID: 1400-SET-2019F-03
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Shahad Yahya Alzahrani

Keywords:

Cloud Computing, Energy Consumption, Integration, WSN.

Abstract:

Wireless sensor networking is the emerging field of research which is responsible for the next-generation technology called Internet of things (IoT). Requirements of typical WSN applications are defined and consist of multi-hop routing, calculation of execution time, power and memory consumption of nodes. Wireless Sensor Networks (WSNs) have many constraints, such as low power resources, limited data storage, and limited areas of access. On the other hand, Cloud Computing (CC) has solved many of these issues by providing data storage in the cloud and streaming that data. Integration of these technologies needs further study to investigate the energy consumption issues. In this thesis, we examined integration of WSN with Cloud Computer to improve WSNs with more energy savings and with an efficient subvention scheme through the cloud services. Various WSN configurations integrated with cloud networks through remote connections showed performance improvements in terms of energy consumption and WSN network lifetime. We used the Cooja application of Contiki OS, which fulfils all requirements like multi-hop routing, memory and power consumption and is found feasible for WSN application development and testing.

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Performance Analysis in Cluster-based VANET

Thesis ID: 1400-SET-2019F-04
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Hanan Homidi AL-Malki

Keywords:

N/A

Abstract:

Nowadays, there are many countries started adopting the vehicle based wireless communications for different purposes such as traffic monitoring, security, etc. The concept of VANET (Vehicular Ad-Hoc Network) is a network in which moving vehicular as nodes in the network in order to form the mobile network. Therefore, VANETs are dynamic mobile ad hoc network (MANET) topology. Basically, there are two main types of VANET infrastructure such as distributed and centralized. The ad hoc and cellular technologies are grouped in centralized infrastructure called Vehicle to Infrastructure (V2I). The distributed architecture is completely based on ad hoc methodologies called as Vehicle to Vehicle(V2V) communication. The focus of our work is on V2V. For V2V VANETs, there is a need for accurate estimation of vehicle nodes location. The GPS based approach is having limitations of unavailability and hence is not reliable for nodes position estimation. There are many research solutions proposed recently for efficient estimation of vehicle nodes position but suffered from the limitations of efficiency. In this project, we proposed a novel approach for location estimation called ESCL-VNET (Extended Self-Correcting Localization scheme for V2V Networks) which is based on recent SCL-VNET method. We first designed a novel received signal strength indication (RSSI) based location estimation method. Self-correction is performed to minimize the location errors by learning network topology scenarios. As this method is not enough to address all environmental conditions, we then proposed the weighted localization using the signal to the interference-noise ratio (SINR) to achieve the reliability and more accuracy in location estimation. The experimental evaluation of the proposed approach is done using NS3 simulation tool. The results show that ESCL-VENT achieved the improved accuracy and performance as compared to existing SCL-VENT.

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Automatic Classification of Brain Tumor and Alzheimer's Disease in MRI

Thesis ID: 1400-SET-2019F-05
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Bashayer Fouad Marghalani

Keywords:

Alzheimer's, tumor, brain, Bag of Features, brain MRI, tumor segmentation, machine learning, computer vision, deep learning, Support Vector Machine, Convolutional Neural Networks, Speeded Up Robust Features, median filter.

Abstract:

Computer vision (CV) and image processing techniques aim at the fast development of medical images diagnoses field. As the specialist takes a long time to diagnose one MRI images, CV techniques and machine learning algorithms make the process faster than the manual method. Consequently, these techniques save time and effort. In this thesis, an intelligent method has been used for the detection and classification of brain pathologies like tumors, Alzheimer's disease (AD), and healthy brain images. The algorithm used encompasses 4 stages: Magnetic Resonance Imaging (MRI) image acquisition, pre-processing, feature extraction, and classification. In this thesis, the Bag of Features module has been used for the classification of the MRI of brain with tumor, MRI of brain of Alzheimer's disease patients, and MRI of normal brain. In this thesis, the average classification accuracy achieved for all three classes is 98%. Furthermore, this thesis has got 98% sensitivity and 99% specificity.

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Automatic Inspection Of The External Quality Of The Date Fruit

Thesis ID: 1400-SET-2019F-06
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Aisha yahya hakami

Keywords:

computer vision, fruit classification, bag of feature, machine learning, date inspection, k mean clustering, SURF descriptor, SVM supported vector machine, error correcting output codes (ECOC), date fruit

Abstract:

Assessing the quality of date fruits manually is a labor-intensive task. Furthermore, the quality of the date fruits in storage may degrade with time and, therefore, it is important to inspect the quality of date fruits routinely. After date harvesting, industrial companies initiate the inspection of dates, a process through they isolate the damaged or defected dates from the good and healthy ones. Industrial factories demand a high-quality and fast production that can be achieved through automatic inspection. In this study, we developed a method of inspecting the external quality of khalas date fruit through image processing. Images of date fruits were classified into good-quality fruits and sugar-defect fruits by using a Bag-of-Feature (BOF). The methodology comprises five main stages including key-points detection, feature(s) extraction, creating a dictionary, vector quantization, and classification. The developed framework was tested on the dataset that was collected by the authors using two types of key-points detection methods. The best classification accuracy for the best grid search was 99% and 84% for the SURF-based key-points detection method on the testing dataset. This research we published in ELSEVIER.

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Improving Counting-Based Secret Sharing Technique for Practical Performance and Remembrance

Thesis ID: 1400-SET-2019F-07
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Taghreed Mohammed Daif Allah Alkhodaidi

Keywords:

N/A

Abstract:

Information security is an area of attention to protect data against infringing uses and prevent unauthorized access. It involves different techniques for protecting sensitive data such as cryptography, steganography. The secret sharing technique is used in cryptography to ensure secure access to information. This thesis focuses on the Counting-based secret sharing technique is a new and simplest secret sharing technique that is developed for multimedia applications and depends on multiple shared keys. Counting-based secret sharing scheme faced some security and efficiency challenges which occur when the target key has a small size, which affects the security as well as making limitations within the number of generated shares, limiting the number of participants. Furthermore, shares keys contain only zeros and ones, making it difficult to remember the key by the participant. This work introduces a new algorithm to increase the number of shares by increasing the size of the target key to generate an unlimited number of shares. Also, this work proposes to add additional zeros within TKs proposed via seven algorithms to increase the number of shares. For shares remembrance, this work introduces a new steganography algorithm for hiding shares inside an image. We expanded the size of the target key by repeating its value for the first enhancement. The study further added sequential zeros algorithms and separated zeros algorithms for second enhancement. For steganography, work proposes LSB redistribution, in which the hiding locations differ each time. The randomness of the shares and target keys adjusted were all measured to select the reliable TK based on NIST 800-22 standard test applied to different target keys sizes such as 16-bits and 64-bits. The study introduced an analysis to show the stego-image quality and security of the proposed method. The research shows interesting results as analysis and comparisons for each presented improvement for increasing the number of shares in which present satisfactory results in terms of security. Also, the results explain that the proposed steganography method showed interesting practical performance and high payload capacity.

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Performance Evaluation of Ad-Hoc Routing Protocols in (FANET)

Thesis ID: 1400-SET-2019M-01
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Anas Ahmed Alkhatieb

Keywords:

IoD, FANETs, Mobility Models, Ad-Hoc Routing Protocols, OPNET, UAVs.

Abstract:

The utilization of Unmanned Aerial Vehicles (UAVs) as aerial relays for the Internet of Drones (IoD) network has several advantages such as civilian and military applications. The collaboration between them becomes a very interesting research topic. A Flying Ad-Hoc Network (FANETs) is a group of Unmanned Aerial Vehicles (UAVs) which can complete their function without human intervention. FANET is considered as a subset of MANET, however, due to high mobility and rapid topology changes in FANET applying routing protocols in FANET is a big challenge. In this thesis, we have extensively evaluated the most of important topology-based routing protocols such as OLSR, AODV, DSR, TORA & GRP in FANET environment. those protocols have been evaluated using an OPNET 17.5 network simulator. We have compared the protocols using Packet loss ratio, end to end delay, Number of hops and throughput in different moving speeds and mobility models like Random Waypoint Mobility (RWPM), Manhattan Grid Mobility Model (MGM), Semi Circular Random Movement (SCRM) and Pursue Mobility Model (PRS). For all evaluation scenarios, the results show that OLSR and GRP perform better than AODV, DSR, and TORA on average. Also, the effect of node mobility on performance is higher than the effect of node speed on performance.

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Improving steganography for hiding secret sharing

Thesis ID: 1400-SET-2019M-02
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Khaled Aedh Alaseri

Keywords:

Information security, Steganography , Arabic text steganography, Kashida steganography, secret bit sharing ,counting based secret sharing.

Abstract:

In this study, the researcher will develop new models to hide data via Arabic text steganography to hide shares generated from target key within texts to help in the retrieval process. The models are built to serve personal remembrance of secret shares to be used within counting-based secret sharing technique. This research hides secret shares adopting humanized remembrance tool to serve uncontrolled assigned shares which are generated from the security system via automatically authentic target key generation process. The shares in their secret sharing process is a challenge that cannot be memorized unlike the normal password assignment that depends fully on personal selection. Therefore, our models secret shares are proposed to be hidden inside the personally chosen texts utilizing improved Arabic text steganography. This steganography models study is based on Kashida extension character used redundantly within Arabic writing text. The research tests our two proposed modifications to original Arabic text steganography all serving secret sharing on the same text database. The comparisons examined the different models on the same benchmark of 40 standard text statements (40 Prophet Hadiths) showing interesting results and promising research contributions.

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TRADE-OFF ANALYSIS FOR GENERIC-POINT PARALLEL ELLIPTIC CURVE SCALAR MULTIPLICATION

Thesis ID: 1400-SET-2019M-03
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Faris Fawzan Al-Otaibi

Keywords:

N/A

Abstract:

Several methods have been proposed to accelerate generic- point elliptic curve parallel scalar multiplication, including pre- com-putation-based methods and postcomputation-based methods. The methods proposed in the literature use key partitioning and process the key partitions via parallel processors. However, the best number of key partitions that would yield the best performance has yet to be investigated. Accordingly, this thesis conducts a trade-off analysis of all methods with different key sizes, numbers of processors and numbers of requests for generic- point elliptic curve parallel scalar multiplication. Furthermore, it proposes a new method and tests against the others. This new method demonstrates the best execu-tion time in most cases.

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An advanced security scheme for users’ authentication over e-learning platform

Thesis ID: 1400-SET-2019M-04
Year: 2019
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Abdulaziz Saleh Alqahtani

Keywords:

N/A

Abstract:

N/A

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Detection And Classification Of Apple Diseases

Thesis ID: 1400-SET-2020F-01
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Asmaa Ghazi Alharbi

Keywords:

Apple Fruit, Apple Scab, Apple Blotch, Apple Rot, Convolutional Neural Network, Deep learning.

Abstract:

In agricultural products, fruit diseases could lead to economic loss. In this thesis, we focus on an important fruit—apples. Disease classification could be done by a human expert, which is the old way, costs a lot of money, and is also time-consuming. Computer vision (CV) and deep learning techniques show promising results with good accuracy and less time. In this thesis, we have considered apple diseases like apple scab, apple blotch, and apple rot; these are fungal diseases. The dataset of the apples were collected from the local market; from that sample, we picked the apples which were already infected. Different models based on convolutional neural network are used for the classification. All the models showed good classification accuracy on more than 93% on testing images. The best accuracy was achieved by model-5; it gave 99.38%.

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Bidirectional Recurrent Neural Networks for Human Motion Prediction

Thesis ID: 1400-SET-2020F-02
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Amal Fahad Al-aqel

Keywords:

N/A

Abstract:

Human motion prediction aims to forecast the most likely future frames of motion conditioned on a given sequence of frames. Because of its importance to many applications especially robotics, human motion prediction has received a lot of interest and has become an active area of research. Recently, deep learning methods have been dominant in many tasks due to their successful results. Particularly, Recurrent Neural Networks (RNNs) have shown excellent performance on human motion prediction task and other tasks that depend on sequential data, where preserving the order of the sequence items is crucial. The well-known Sequence-to-Sequence (Seq2Seq) architectures have been used for sequence learning where two RNNs namely the encoder and the decoder work cooperatively to transform one sequence to another. In the context of neural machine translation, the use of attention decoders yields state-of-the-art results. This work attempts to assess quantitatively the use of a bidirectional encoder and an attention decoder in human motion prediction. The experiments of this work have shown that using attention decoder has achieved state-of-the-art results after 160 milliseconds of motion prediction. In contrast with earlier works, the quality of predictions doesn’t deteriorate and remains stable even after more than 1 second of motion prediction.

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Performance of Wireless Sensor Network Medium Access Control for Monitoring

Thesis ID: 1400-SET-2020F-03
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Ameera Saad Alharthi

Keywords:

Wireless personal sensor network, IEEE802.15.4, GTS, sleep cycle, Throughput.

Abstract:

Recently, applications requirements are growing at a rapid rate. They are constantly being developed, altered, and improved upon. Achievement of these applications requirements depends on the design of the protocols of Medium Access Control (MAC) layer. Therefore, one of these reliable protocols is IEEE802.15.4 beacon-enabled mode that is considered as a de facto protocol, and it is widely implemented in the monitoring field. The protocol is designed to Low-Rate Wireless Personal Area Networks (LR-WPANs) with limited power. In addition, IEEE802.15.4 MAC uses a superframe that is divided into two periods, Contention Access Period (CAP) and Contention Free Period (CFP). Generally, within the monitoring field, the exchange of sensitive data between two nodes occurs during the CFP because it offers real-time guarantees through the Guaranteed Time Slot (GTS) mechanism. However, the 802.15.4 standard has three issues. First, lack of scalability, which is caused by the maximum possible devices that the GTS can allocate which is only seven. Second, in CFP, all timeslots have fixed-length, which leads to a slot sizeinduced bandwidth waste problem. Third, its duty cycle is not efficient, especially under very high duty cycle. In our thesis, we have proposed an efficient GTS allocation scheme to eliminate the GTS bandwidth underutilization problem and allows to allocate more than seven devices in same superframe. Our scheme uses variable-length timeslots that are allocated to devices based on their actual bandwidth. Also, we proposed an enhanced standard protocol’s sleeping schedule, aims to conserve sensor energy without compromising the low latency. The underlying idea of our scheme is based on the B-MAC duty cycle with additional modifications. The proposed schemes were evaluated through OMNet++ simulator and the results evidenced that our proposed GTS allocation schemes and sleeping schedule scheme are outperforming the IEEE 802.15.4 standard.

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A Robust Image Watermarking Technique Using Multi-Level Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT)

Thesis ID: 1400-SET-2020F-04
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Ameera Abdullah Al-Shareef

Keywords:

watermarking, DWT, DCT

Abstract:

High quality, robust and effective watermarking techniques are presently required due to rapid utilization and growth of digital data on the internet. Watermarking can be defined as a technique that provides security to digital data, such as videos, audios and images. Digital watermarking plays a vital role in safeguarding against illegally multimedia copying. The watermark comprises secret information related to ownership, origin and copy control. The information is embedded in the multimedia content, responsible for issues related to imperceptibility and robustness. This project introduces a novel digital watermarking algorithm. The basis of the new technique is multi-level Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT). In the proposed method, the variable visibility factor is employed to embed watermarks of the host image with low-frequency components. The state-of-the-art algorithm performance will be prepared with the watermarking algorithm proposed using the Peak Signal to Noise Ratio (PSNR) parameters as the benchmark. If the watermark utilized is weak, the hidden watermark image would be lost. However, if the watermark is robust, it would be able to survive the intended or unintended attack. A good watermarking technique should possess the following key characteristics:  High robustness  Protection of copyright  High invisibility  Degradation of low quality

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Event Detection in Social Media Within the Arabic Language

Thesis ID: 1400-SET-2020F-05
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Batool Mohammed Maher Hamawi

Keywords:

Arabic tweets, Saudi dialect, Twitter, Event detection, Classification.

Abstract:

The rise of social media platforms makes it a valuable information source of recent events and users’ perspective towards them. Social media platforms have been recently exploited as a valuable source of information for event detection. Event detection, one of the information extraction aspects, involves identifying specified types of events in the text. The recent increase of real-world events number that is disseminated over Twitter, which is one of the most important communication platforms in recent years; has attracted researchers to utilize tweets for the event detection system. In this research, we introduce FloDusTA, Flood, Dust Storm, Traffic Accident Saudi Event which is a dataset of tweets that we have built for the purpose of developing an event detection system. The dataset contains tweets written in both Modern Standard Arabic and Saudi dialect. We focus on the flood, dust storm, and traffic accident events according to their significant influence on human life and economy in Saudi Arabia. FloDusTA, are built based on three main steps, data collection, data cleaning and filtering, and data labeling process. The tweets are labeled with four labels: flood, dust storm, traffic accident, and non-event. The necessity to detect flood, dust storm and traffic accident events effectively and extract events from highly noise tweet content is paramount importance. For such events, it is crucial to obtain good result for the task of detecting events tweets from non-related events tweets. To this aim, we investigate the effectiveness of dividing the problem of event detection into two classification steps. This study explores a two-step approach of performing event and non-event classification on FloDusTA and then classifying the resulted events into specific types of events. Two-step approach compares with one-step approach of doing one multiclass classification for detecting flood, dust storm, and traffic accident event. The experimental evaluation shows that the two-step event detection approach is promising.

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A METHOD OF SKIN DISEASE DETECTION USING IMAGE PROCESSING

Thesis ID: 1400-SET-2020F-06
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Nawal Soliman Alkolify Alenezi

Keywords:

N/A

Abstract:

Skin diseases are more common than other diseases. Skin diseases may be caused by a fungal infection, bacteria, allergy, viruses etc. The advancement of lasers and photonics-based medical technology has made it possible to diagnose skin diseases much more quickly and accurately. However, the cost of such a diagnosis is very expensive. So, image processing techniques have a role in the diagnosis of skin diseases via an automatic diagnosis system for dermatology at an initial stage. The extraction of features has a major role in the classification of skin diseases. Computer vision has a role in the detection of skin diseases in a variety of techniques. Owing to deserts and hot weather, skin diseases are very common in Saudi Arabia. This study contributes to the research on skin disease detection. We have proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area, and uses image analysis to identify the type of disease. Our proposed approach is simple and fast. Most importantly, it does not require expensive equipment other than a camera and a computer. The approach works on the inputs of a color image. The system resizes the input image to extract features using a pre-trained convolutional neural network. After that it classifies feature using multiclass SVM. Finally, the system displays the name of the disease to the user with some tips for the treatment of skin disease. The system detects three different types of skin diseases, namely eczema, psoriasis, and melanoma with a 99% accuracy rate.

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Studying of Cloud Computing Scheduling

Thesis ID: 1400-SET-2020F-07
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Reem Mohammed Ganadily

Keywords:

N/A

Abstract:

The increasing need to provide a lot of services to users in daily life and various sectors like commercial, scientific and educational through internet led to the expansion for the use of cloud processing and virtualization technology. Moreover, cloud environment must insure on-demand service availability by optimizing cloud processing. Unprofitable resource is a type of resource misuse. The complexity of such environment can lead to resource misuse. Such resource misuse happened during job assignment of tasks on resources. Resource management is a key solution to overcome resource misuse. Resource management can be defined as the efficient way of resources organization to be able to use or utilize any resource when needed. Many algorithms are developed to optimize cloudlet distribution on virtual machines (VMs) in cloud processing environment such as First Come First Serve, Min-Max, Shortest Job First, and more. The problem of task distribution optimization is complex problem. For SJF Longer processes will cause more waiting time for cloudlets that waiting in the cloudlet queue, so it will produce low performance job distribution algorithm. So, we have to find a way to reduce waiting time in order to enhance task schedule performance, this will enhance the overall performance. To optimize cloud processing, it is necessary to optimize resource utilization including task scheduling and resource management. Many researchers discuss various algorithms to optimize both resource management and task scheduling. Task scheduling is the operation of distributing the tasks required to be processed on suitable idle virtual machines (resource) to process them conserving efficiency of cloud processing environment. To optimize scheduling operation, we must consider minimizing: Waiting time (the time between task arrival to cloud computing system and the starting time of processing - m sec),Response time (the time between starting and first output time - m sec),Run time (the time between starting and finish time - m sec),Turnaround time (the time between task arrival time and task finish processing time - m sec),Finish time (the time between starting and end processing time - m sec) and Throughput (maximizing) (average number of tasks finished per unit time task/m Sec.). In order to validate our enhanced JSF we used a cloud simulator to simulate virtual cloud processing environment. This virtual environment consists of as many as required of virtual machines VMs to be used for processing simulation and we create a random length cloudlet generator to produce required cloudlet set for test. A final report is generated by cloud simulator include 4 (throughput, waiting time, turnaround time and finish time) parameters go compare between traditional SJF and enhanced algorithm. The proposed algorithm satisfies the above constraints that enhance cloud-processing performance. The proposed algorithm is modified Shortest Job First (SJF) scheduling algorithm where it solves the problem resource misuse by avoiding the idle VMs during the processing of all tasks. The algorithm idea based on detecting the first virtual machine that finishes all tasks assigned to it, in other words its state turns to idle. As soon as the algorithm detects first idle VM, it starts collecting all unprocessed tasks including tasks under execution. Then the algorithm redistributes all unprocessed tasks on all VMs. By this modified algorithm we achieved more significant enhancement in waiting time (measured in m Sec), turnaround time (measured in m Sec) and throughput (measured in task/m Sec.). Two of the parameters that can monitor the amount of enhancement are Throughput that enhanced by value between: 35% to 100%, Finish Time that enhanced by value between: 26% to 52%,

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Combining Light-weight Cryptography with Arabic Text Steganography for Higher Security

Thesis ID: 1400-SET-2020F-08
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Girls
Supervisor: Author:

Malak Gorm Allah Ibrahim Al Khudaydi

Keywords:

N/A

Abstract:

Light-weight Cryptography is a kind of cryptographic primitives that concentrates on optimizing encryption algorithms to run on small and resources-limited devices such as IoT devices. It is different from Conventional Cryptography methods that can work on systems with high power and memory capabilities. Arabic Text Steganography is the method of hiding messages within Arabic Text, so that any intruder cannot even detect there is a message hidden. The integration between the two techniques gives higher security level. The secret message is not only hidden with robust techniques, but is also encrypted with effective cryptographic methods. Some studies have used the idea of the integration between Cryptography and Steganography methods. The work in this thesis added the value of the choice between different cryptographic techniques. The study suggested the usage of the Arabic Feature, all of the Diacritics, instead of using some of them. The work in the thesis have used Light-weight Cryptography (LWC) using three encryption algorithms which are, AES, DES and IDEA. The message is encrypted using one of the three algorithms and it is turned into encrypted message in a form of binary bits. After the message is encrypted, it goes though Arabic Text Steganography phase. The steganography uses all of the diacritics in Arabic to hide the encrypted bits within the cover-text. The chosen cover-text in the study is Arabic historical poetry. The output of the integration is a Stego-file, which includes an encrypted hidden message. The study showed the results from different prospective, which are mainly Security and Capacity. Each aspect is monstrated using different experiment such as Histogram analysis, PSNR and Letters Variations. The experiment have tested the usage of different bits count to record the best case hiding the message. The study added the value of using all of the diacritics and compared it with a previous study of using two diacritics only. The research analyzed the capacity of hidden bits within the historical poetry and within samples from the holy Quran to verify the results. Security analysis has been part of the experiment in the study to check which cryptographic algorithms gives better results and determine the best number of bits to hide. The histogram analysis includes using different letters, starting from two letters up to 16 in the encryption phase using the three algorithm The results have indicated that AES gives better results in terms of capacity. The results have shown that using the full diacritics is better than using two diacritics in terms of capacity and the value changes with the diacritics in the cover text. The security quantity test is suggesting using AES in the 2-layer hiding information. While observing all of the histograms for all the letters used, IDEA gives the nearest histogram compared to the original one. The results of PSNR analysis are showing interesting similar values indicating that all three methods are acceptable from security visual comparisons point of view. The research uses various ways to test the system effectiveness and robustness. It compares the results with similar studies and uses different cover-text. The limitation in the study is the variance of determining which encryption method is the most effective.

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Design and Implementation of a Compact GF(2m) Optimal Normal Basis Field Arithmetic Unit

Thesis ID: 1400-SET-2020M-01
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Husam Ibrahim Rashad Aldoobi

Keywords:

N/A

Abstract:

This thesis proposes the design of an area-efficient compact optimal normal basis field arithmetic unit (FAU) utilizing the common parts between the Massy-Omura multiplier and the Itoh -Tsugii inverter. The field arithmetic operations include addition, multiplication, and inversion. Addition can be easily implemented as an XOR of the corresponding vectors. Multiplication typically requires more computational time than addition, and it has more circuit complexity. Multiplicative inversion can be conducted by repeatedly applying the multiplication squaring algorithm. The design showed decreased hardware complexity and a decrease in the number of inputs compared to the standard approach, which makes the design very attractive when implementing elliptic curve cryptosystems in resource-constrained devices such as, smart cards, radio-frequency identification (RFID), and wireless sensor networks. The design was initially run on 173-bit input; it was then adjusted to run on 233, 350, and 515-bit inputs. The proposed design was coded using VHDL on Xilinx’s ISE design suit 14.5 and simulated on an Artix7 XC7A200T field-programmable gate array (FPGA).

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Using Machine Learning to Improve Evolutionary Multi-Objective Optimization

Thesis ID: 1400-SET-2020M-02
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Rakan Saad ALotaibi

Keywords:

N/A

Abstract:

Multiobjective optimization problems (MOPs) arise in many real-world applications. MOPs involve two or more objectives with the aim to be optimized. With these problems im- provement of one objective may led to deterioration of another. The primary goal of most multiobjective evolutionary algorithms (MOEA) is to generate a set of solutions for approx- imating the whole or part of the Pareto optimal front, which could provide decision makers a good insight to the problem.Over the last decades or so, several different and remarkable multiobjective evolutionary algorithms, have been developed with successful applications. However, MOEAs are still in their infancy. The objective of this research is to study how to use and apply machine learning (ML) to improve evolutionary multiobjective opti- misation (EMO). The EMO method is the multiobjective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D has become one of the most widely used algo- rithmic frameworks in the area of multiobjective evolutionary computation and won has won an international algorithm contest.

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MULTI-OBJECTIVE EVOLUTIONARY TRAINING SET SELECTION FOR ARTIFICIAL NEURAL NETWORKS

Thesis ID: 1400-SET-2020M-03
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Sanad Hamid Alslayhbi

Keywords:

N/A

Abstract:

N/A

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Software process metrics in Saudi Arabian enterprises: a case study

Thesis ID: 1400-SET-2020M-04
Year: 2020
Type: Thesis

Department: Computer Science and Engineering
Program: Computer Science and Engineering
Division: Boys
Supervisor: Author:

Wael Ayidh M Alrabie

Keywords:

N/A

Abstract:

This dissertation is based on the application of software metrics in use by software companies and implemented by the Saudi Arabian government. The aim of the dissertation is to explore the software metrics used by various companies. The software companies use normal structures to evaluate and produce the services required by the customers. The Government issued many policies and regulations to maintain a productive process. The introduction of ISO 9001, Agile, and CMMI policies have been made mandatory by the government. The methodology of this dissertation is based on primary data extracted by conducting interviews with six different companies and their employees. The data extracted is essential for the researcher to evaluate the issues and promote advancement of software metrics in Saudi Arabia. The results obtained by the dissertation show that companies need to give more attention to t quality and productivity management. Moreover, the findings indicate that when agile development is undertaken through software effectiveness, the company’s services are implemented appropriately. Furthermore, this dissertation reports that a Level 2 maturity grade was necessary to take place for the Software Engineering Institute suggested Capability Maturity Model Integration (CMMI)

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