Thesis Title: Enhanced AODV protocol against Black Hole Attack based on Classification Algorithm
Author: Al Mohannad Mohsen Ali Athamneh, Supervisor: Dr. Hasan Al-Refai, Year: 2018
Faculty: Information Technology, Department: Computer Science

Abstract: Covering areas that do not have ready infrastructures to support network connections, has become one of the most demand topics. Mobile ad hoc network (MANET). Were the solution for this problem, were protocols like Ad Hoc On-Demand Distance Vector (AODV) take action to manage the devices inside. As a result of such network characteristics AODV protocol were vulnerable to list of attacks like denial-of-service (DOS). Attacks which contain attacks like black hole and worm whole attacks. In this research new algorithm for black hole attack detection and prevention were presented based on four features extracted from the AODV protocol which are (Total number of Packets Dropped, Route Error, Rout Reply, and sequence number) .Ready data set was used to select best features related to black hole attack using symmetrical uncertainty (SU) feature selector based on WEKA tool. The four features were evaluated by classification method called J48 in order to proof their capability of defining black hole nodes. Moreover, the presented algorithm implements very simple test classification criteria over the regular AODV protocol to allow detection and sharing the black nodes identities. The results of the experimental results were implemented using Global Mobile Information System Simulator(GlomoSim) simulator over 50 nodes, and compared to 3 other previously proposed algorithms (Standard AODV, behavior-driven development (BDD-AODV), and Artificial neural network Ad Hoc On-Demand Distance Vector(ANN-AODV), where the new proposed algorithm proofed its capability in detecting and preventing black hole attacks with higher efficiency average on END-TO-END time delay, lower overhead factor, and higher packet delivery ration packet delivery ratio (PDR) than all other three algorithms.

Keywords: AODV, Protocol, Black Hole, Attack,Network

Thesis Title: اضطراب الأداء اللغوي لدى مرضى التوحد
Author: علا خالد عيد الخوالدة, Supervisor: الدكتور يوسف ربابعة, Year: 2018
Faculty: Arts, Department: Arabic Language and Literature

Abstract: تعد هذه الدراسة واحدة من أهم الظواهر اللغوية المهمة في اللغة، ألا وهي ظاهرة التواصل الإنساني الممثلة باللغة البشرية، التي نعبر من خلالها عن الأفكار والآراء ونقل الملعومات والخبرات.

Keywords: ظاهرة، تواصل إنساني، لغة بشرية

Thesis Title: Modifid Multi-Level Steganography to Enhance Data Security
Author: Shadi Elshare, Supervisor: Nameer N. EL-Emam, Year: 2018
Faculty: Information Technology, Department: Computer Science

Abstract: Data-hiding using steganography algorithm becomes an important technique to prevent unauthorized users to have access to a secret data. In this thesis, proposed steganography algorithm has been constructed to hide as much as possible from the load of secret data in a color and a gray images, this algorithm is named Deep Hiding Extraction Algorithm (DHEA) to modify Multi-Level Steganography (MLS). This algorithm is based on Modified Least Significant Bit (MDLSB) to scatter data in a cover image. The proposed DHEA algorithm defines a number of levels randomly; where each level using a gray image except the last level that uses a color image. Furthermore, proper randomization approach with two layers have been implemented; the first layer uses random pixels selection for hiding a secret data at each level while the second layer implements at the last level to move randomly from segment to the other. In addition, the proposed approach implements an effective lossless image compression using DEFLATE algorithm to make it possible to hide data into a next level. Dynamic encryption algorithm based on Advanced Encryption Standard (AES) has been applied at each level by changing cipher keys from one level to the next to increase the security and working against attackers. Soft computing with a meta-heuristic approach using Artificial Bee Colony (ABC)algorithm is introduced to achieve smoothing on pixels in image, this approach is effective to reduce the noise caused by a hidden large amount of data and to increase the stego-image quality on the last level. The experimental result demonstrates the effectiveness of the proposed algorithm DHEA and to show high-performing to hide a large amount of data up to 4-bpp (bits per pixel) with high security in terms of hard extraction of a secret message and noise reduction of the stego-image. Moreover, using deep hiding with unlimited levels is promising to confuse attackers and to compress a deep sequence of images into one image.

Keywords: Steganography, Multi-level steganography, Bee Colony Algorithm, least Significant Bit, Image Smoothing, Segmentation Image

Thesis Title: System Identification of Quadcopter Using Experimental Data
Author: Iyad Mahmoud Salameh, Supervisor: Dr. Tarek A. Tutunji, Year: 2018
Faculty: Engineering, Department: Mechatronics Engineering

Abstract: An unmanned areal vehicle, Quadcopter, is assembled and tested to develop suitable mathematical models for takeoff and hovering operations. The mathematical dynamic system is highly nonlinear, complex, and inherently unstable. Therefore, System Identification (SysID) methods are used to obtain the mathematical model of the physical system. The models’ outputs are the altitude and the angular velocities (roll, pitch, and yaw). The models’ inputs are the voltage control signals sent to the motors. Two types of models are considered: Single-Input Single-Output (SISO) and Multiple-Input Single-Output (MISO). The input-output data is collected from flight tests, then logged onto MATLAB where the models are developed using two different methods. In the first one, SysID Toolbox in MATLAB is used to develop Auto-Regressive eXogenous (ARX) transfer function. In the second one, Artificial Neural Networks (ANN) are used to construct transfer functions using NN2TF algorithm. The models are compared and validated using different sets of data, and the models with the lowest error are selected. Furthermore, simulation results for the ARX and NN2TF methods are compared, where it is shown that the derived models using NN2TF method have lower orders with small errors.

Keywords: Quadcopter, SysID, SISO, MISO, MATLAB, ARX, NN2TF algorithm

Thesis Title: Neural Network Control for Enhanced Response of Thyristor Controlled Reactor Compensator
Author: Dana Mohammed Rafat Ragab, Supervisor: Dr. Jasim Ghaeb, Year: 2018
Faculty: Engineering, Department: Mechatronics Engineering

Abstract: In this work, a Neural Network Control (NNC) is proposed for load voltage balancing in a three-phase electrical power system. The Neural Network (NN) is suggested to determine the appropriate set of firing angles required for the Thyristor Controlled Reactor (TCR) to balance the three load voltages accurately and quickly. In order to validate the performance of the proposed NNC, Aqaba-Qatrana- Amman South (AQAS) power system is considered as a case study and both MATLAB/Simulink and laboratory model are built. Different feeding techniques to irrigate the NN with input data are proposed; RMS values of the three load voltages (RLV), RMS values of the space vector of three load voltages (RSV) and RMS values of both three load voltages and their space vector (RLVSV). These techniques compromise between reducing the measured load parameters and providing qualitative data about system status. Therefore, both the number of required NNs and the complexity of NN structure are reduced significantly. Thus, the response time of the NNC is enhanced and the required firing angles are provided to the TCR in 10 ms for 50 Hz system frequency. It is worth to mention that all calculations associated with feeding techniques are performed in terms of the proposed space vector signal, which has twice of the system frequency. In this work, it is proved that in case of unbalance three phase load change, the variation of space vector takes a sinusoidal form. Furthermore, several simulation and experimental test cases are considered to examine the capability of the NN for generating the required set of firing angles based on Voltage Unbalance Factor (VUF) performance metric. The results show that NN with RLV and RLVSV feeding techniques provides a satisfactory performance in unbalance mitigation compared to the well-established NN techniques.