tuka kareem jebur
@uomustansiriyah.edu.iq
computer science
Mustansiriyah University
RESEARCH INTERESTS
wireless network, neural network, data mining,wireless security
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Tuka Kareem Jebur
International Association of Online Engineering (IAOE)
Security and safety are critical concerns in Vehicular Adhoc Networks. vulnerable to Distributed Denial of Service (DDoS) attacks, which occur when multiple vehicles carry out various tasks. This cause disrupts the normal functioning of legitimate routes. In this work, the Hybrid PSO-BAT Optimization Algorithm (HBPSO) Algorithm based on modified chaos -cellular neural network (Chaos - CNN) approaches has been proposed to overcome DDoS attacks. The suggest approaches consists of three-part which are hybrid optimization search algorithm to enhance the route from source to destination, chaos theory module is used to detect the abnormal nodes, then on Modified Chaotic CNN (MCCN) employed to prevent a malicious node from sending data to the destination by determining node that consumer more resource, packets lose or the victim could reset the path between the attacker and itself. CICIDS dataset has been used to test and evaluate the performance of the proposed approach based on the criteria of accuracy, packet loss, and jitter. The Chaos - CNN approached results to outperform similar models of the related work and the approach protects the VANETs with high accuracy of 0.8736, specificity of 0.9959, TPR of 0.9561, and FPR of 0.78, Detection rate 0.9561.
Tuka Kareem Jebuer
Taru Publications
Abstract A mobile ad hoc network (MANETs) is a collection of moving nodes that combine into a network with no predefined infrastructure. There are many types of attacks that could target MANETS, one among them is Distributed Denial of service attacks (DDoS). DDoS is defined as attacking routing functions and taking down the entire operation of the mobile ad hoc network. The two primary victims of DDoS attacks are the functions of routing and battery capacity. The DDoS attack can cause routing table overflow which in turn can potentially cause the infected node floods. The routing overflow is followed by creating a fake route packet to consume the available resources of the participating active nodes. This cause disrupts the normal functioning of legitimate routes. In recent years, different approaches are implemented to improve the security level of MANET. In this work, the Cuckoo Search Algorithm-based Modified Elman’s Neural Network (CSA - MENN) approaches have been proposed to overcome DDoS attacks. The CSA - MENN approaches consists of three-part which are Cuckoo search algorithm clustering area to enhance the route from source to destination, chaos theory module is used to detect the abnormal nodes, then the Modified Elman Neural Network (MENN) is employed to prevent a malicious node from sending data to the destination by determining node that consumed more resources. Packets could be lost or the victim could reset the path between the attacker and itself. CICIDS dataset has been used to test and evaluate the performance of the proposed approach based on the criteria of accuracy, packet loss, and jitter. The data set, CICIDS 2017, used in this article divides the data into 7 groups: 5 for training, 1 for validation, and 1 for generalization. In summary, approximately 71.4 percent of data is used for training and 28.6 percent for validation and generalization.
Tuka Kareem Jebur
International University of Sarajevo
Haider K Hoomod and Tuka Kareem Jebur
IOP Publishing
Several methods have been developed for routing problem in MANETs wireless network, because it considered very important problem in this network ,we suggested proposed method based on modified radial basis function networks RBFN and Kmean++ algorithm. The modification in RBFN for routing operation in order to find the optimal path between source and destination in MANETs clusters. Modified Radial Based Neural Network is very simple, adaptable and efficient method to increase the life time of nodes, packet delivery ratio and the throughput of the network will increase and connection become more useful because the optimal path has the best parameters from other paths including the best bitrate and best life link with minimum delays. The results show how the proposed routing algorithm produces higher speed comparing with Dijkstra algorithm and finds the optimal path in addition to shortest path. Proposed routing algorithm depends on the group of factors and parameters to select the path between two points in the wireless network.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
Applying self-organizing map and modified radial based neural network for clustering and routing optimal path in wireless network
HK Hoomod, TK Jebur
Journal of Physics: Conference Series 1003 (1), 012040
8 2018
FINDING OPTIMAL AND RELIABLE PATH IN MOBILE SINK WIRELESS SENSOR NETWORK BY APPLYING GENATIC OPTMIZATION CELLULER NEURAL NETWORK(GO-CNN)
T jebuer
Journal of Engineering Science and Technology
2021
HPSGNN: A Hybrid of Particle Swarm and Genetic Neural Network System to Defense Against Blackhole Attack Targeting MANETs
T jebuer
Proceedings of the 1st International Conference on Computing and Emerging …
2021
Implantation modified deep echo state neural networks and improve harmony clustering algorithm for optimal and energy efficient path in mobile sink
T jebuer
Periodicals of Engineering and Natural Sciences 9 (1), 48
2021
Review: application and challenge IN Internet of Things IOT
T jebuer
International Journal of Psychosocial Rehabilitation 24 (7), 4300-4303
2020
Applying Modified Genetic Radial Basis Function Neural Network (MGRBFNN) to Predicting exchange rate future movements (An Empirical Study on a sample of currencies traded in …
TKJ Abdulkadhim M. Queen1 Kafaa Ali Alborgeef*2
Journal of Xi'an University of Architecture & Technology 6121 (XII)
2020
Using Zone-Sampling Based Trace Back Algorithm and Modified Echo State Networks to Detect and Prevent Denial-of-Service Attacks in MANETs
T jebuer
FILOMAT JOURNAL 33 (2406-0933)
2019
Modified Radi