International Journal of Computer Networks and Applications

Open Access Open Access  Restricted Access Subscription Access

Detection of Black and Grey Hole Attacks Using Hybrid Cat with PSO-Based Deep Learning Algorithm in MANET


Affiliations
1 Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamil Nadu, India., India
2 Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India, India
3 Electronics and Communication Department, Saranathan College of Engineering, Trichy, Tamil Nadu,, India
 

.The newest example of wireless networks, known as mobile ad hoc networks (MANETs), offers some qualities, including a topology that can change dynamically, a baseless network, a range of transmission, a routing procedure, and reliability. In a black hole attack on a computer network, packets are deleted as opposed to being forwarded through a router. This often happens when a router has been corrupted by several circumstances. A routing attack called a "black hole" has the power to bring down an entire network. One of the most common types of assaults on MANETs is the Grey Hole Attack, in which a hostile node allows routing but prevents data transmission. MANET security is a top priority because they are far more susceptible to assaults than wired infrastructure. This study focused on detecting black and grey-hole attacks in MANET by using deep learning techniques. The forwarding ratio metric is used in the individual attack detection phase to distinguish between the defective and normal nodes. The encounter records are manipulated by malicious nodes in the collusion attack detection phase for escaping the detection process. The attacks are detected by using different deep learning techniques like Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The parameter tuning operation is carried out by using the Hybrid Cat-Particle Swarm Optimization (HCPSO). The simulation results shown in our proposed system detect with better accuracy.

Keywords

Black Hole Attack, Convolutional Neural Network, Mobile Ad-Hoc Networks, Long Short-Term Memory, Hybrid Cat-Particle Swarm Optimization, Grey Hole Attacks.
User
Notifications
Font Size

  • Moudni, H., Er-rouidi, M., Mouncif, H. and El Hadadi, B., 2019. Black hole attack detection using fuzzy based intrusion detection systems in MANET. Procedia Computer Science, 151, pp.1176-1181.

  • Gurung, S. and Chauhan, S., 2020. A survey of black-hole attack mitigation techniques in MANET: merits, drawbacks, and suitability. Wireless Networks, 26(3), pp.1981-2011.

  • Eswaran, S., Rani, V., D., D., Ramakrishnan, J., & Selvakumar, S. (2021). An enhanced network intrusion detection system for malicious crawler detection and security event correlations in ubiquitous banking infrastructure. International Journal of Pervasive Computing and Communications, 18(1), 59–78. https://doi.org/10.1108/ijpcc-04-2021- 0102.

  • Manoranjini, J., Chandrasekar, A. and Jothi, S., 2019. Improved QoS and avoidance of black hole attacks in MANET using trust detection framework. Automatika: časopiszaautomatiku, mjerenje, elektroniku, računarstvoikomunikacije, 60(3), pp.274-284.

  • Yasin, A. and Abu Zant, M., 2018. Detecting and isolating black-hole attacks in MANET using timer based baited technique. Wireless Communications and Mobile Computing, 2018.

  • Sadhana, S., Sivaraman, E., & Daniel, D. (2021). Enhanced EnergyEfficient Routing for Wireless Sensor Network Using Extended PowerEfficient Gathering in Sensor Information Systems (E-PEGASIS) Protocol. Smart Systems: Innovations in Computing, 159–171. https://doi.org/10.1007/978-981-16-2877-1_16.

  • Gurung, S. and Chauhan, S., 2018. A dynamic threshold based approach for mitigating black-hole attack in MANET. Wireless Networks, 24(8), pp.2957-2971.

  • Thanuja, R. and Umamakeswari, A., 2019. Black hole detection using evolutionary algorithm for IDS/IPS in MANETs. Cluster computing, 22(2), pp.3131-3143.

  • Rajendran, N., Jawahar, P.K. and Priyadarshini, R., 2019. Cross centric intrusion detection system for secure routing over black hole attacks in MANETs. Computer Communications, 148, pp.129-135.

  • Daniel D., Preethi N., Jakka, A., & Eswaran, S. (2021). Collaborative Intrusion Detection System in Cognitive Smart City Network (CSCNet). International Journal of Knowledge and Systems Science, 12(1), pp.60–73, https://doi.org/10.4018/ijkss.2021010105.

  • Panda, N. and Pattanayak, B.K., 2018. Energy aware detection and prevention of black hole attack in MANET. International Journal of Engineering and Technology (UAE), 7(2.6), pp.135-140.

  • Abood, M.S., Mahdi, H.F., Hamdi, M.M., Ibrahim, O.J., Mohammed, R.Q. and Ahmed, S.F., 2020, December. Black/Gray Holes Detection Tools in MANET: comparison and analysis. In 2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-8). IEEE.

  • E. Sivaraman, "Dynamic cluster broadcasting for Mobile Ad Hoc Networks," 2010 International Conference on Communication and Computational Intelligence (INCOCCI), 2010, pp. 123-127.

  • Arul Selvan, M. and Selvakumar, S., 2019. Malicious node identification using quantitative intrusion detection techniques in MANET. Cluster computing, 22(3), pp.7069-7077.

  • Bhuvaneswari, R. and Ramachandran, R., 2019. Denial of service attack solution in OLSR based manet by varying number of fictitious nodes. Cluster Computing, 22(5), pp.12689-12699.

  • Prasanna, D.J.D., Aravindhar, D.J. and Sivasankar, P., 2021. Block Chain based Grey Hole Detection Q Learning based CDS Environment in Cloud-MANET. Webology, 18(SI01), pp.88-106.

  • Hassan, Z., Mehmood, A., Maple, C., Khan, M.A. and Aldegheishem, A., 2020. Intelligent detection of black hole attacks for secure communication in autonomous and connected vehicles. IEEE Access, 8, pp.199618-199628.

  • Rani, P., Verma, S., Rawat, D.B. and Dash, S., 2022. Mitigation of black hole attacks using firefly and artificial neural network. Neural Computing and Applications, pp.1-11.

  • Sathyaraj, P. and Kannan, K., 2021. Host based Detection and Prevention of Black Hole attacks by AODV-ICCSO Algorithm for security in MANETs.

  • Janakiraman, S., Deva Priya, M., Aishwaryalakshmi, G., Suganya, T., Sam Peter, S., Karthick, S. and Christy Jeba Malar, A., 2022. Improved Rider Optimization Algorithm-Based Link Aware Fault Detection (IROA-LAFD) Scheme for Securing Mobile Ad Hoc Networks (MANETs). In 3rd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (pp. 155-169). Springer, Cham.

  • Rani, P., Verma, S. and Nguyen, G.N., 2020. Mitigation of black hole and gray hole attack using swarm inspired algorithm with artificial neural network. IEEE Access, 8, pp.121755-121764.

  • Ramachandran, D., Rajeev Ratna, V., PT, V.R. and Garip, I., 2022. A Low-Latency and High-Throughput Multipath Technique to Overcome Black Hole Attack in Mobile Ad Hoc Network (MTBD). Security and Communication Networks, 2022.

  • Srinivasan, V., 2021. Detection of Black Hole Attack Using Honeypot Agent-Based Scheme with Deep Learning Technique on MANET. Ingénierie des Systèmesd'Information, 26(6).

  • Liu, J., Jiang, X., Nishiyama, H. and Kato, N. (2013a) ‘On the delivery probability of two-hop relay MANETs with erasure coding’, IEEE Transactions on Communications, Vol. 61, No. 4, pp.1314–1326.

  • Huang, H. and Zhou, Q. (2012) ‘Petri-net-based modeling and resolving of black hole attack in WMN’, The IEEE 36th Annual Computer Software and Applications Conference Workshops,Izmir, Turkey, pp.409–414.

  • Charles E. Perkins, and Elizabeth M. Royer, “Ad-hoc On-Demand Distance Vector (AODV) routing,” Internet Draft, November 2002.

  • A. Shevtekar, K. Anantharam, and N. Ansari, “Low Rate TCP Denialof-Service Attack Detection at Edge Routers,” IEEE Commun. Lett., vol. 9, no. 4, Apr. 2005, pp. 363–65.

  • Mohammad Al-Shurman and Seong-Moo Yoo, Seungjin Park, “Black hole Attack in Mobile Ad Hoc Networks” Proceedings of the 42nd annual Southeast regional conference ACMSE 42, APRIL 2004, pp. 96- 97.


Abstract Views: 141

PDF Views: 1




  • Detection of Black and Grey Hole Attacks Using Hybrid Cat with PSO-Based Deep Learning Algorithm in MANET

Abstract Views: 141  |  PDF Views: 1

Authors

S. Venkatasubramanian
Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Chidambaram, Tamil Nadu, India., India
A. Suhasini
Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India, India
S. Hariprasath
Electronics and Communication Department, Saranathan College of Engineering, Trichy, Tamil Nadu,, India

Abstract


.The newest example of wireless networks, known as mobile ad hoc networks (MANETs), offers some qualities, including a topology that can change dynamically, a baseless network, a range of transmission, a routing procedure, and reliability. In a black hole attack on a computer network, packets are deleted as opposed to being forwarded through a router. This often happens when a router has been corrupted by several circumstances. A routing attack called a "black hole" has the power to bring down an entire network. One of the most common types of assaults on MANETs is the Grey Hole Attack, in which a hostile node allows routing but prevents data transmission. MANET security is a top priority because they are far more susceptible to assaults than wired infrastructure. This study focused on detecting black and grey-hole attacks in MANET by using deep learning techniques. The forwarding ratio metric is used in the individual attack detection phase to distinguish between the defective and normal nodes. The encounter records are manipulated by malicious nodes in the collusion attack detection phase for escaping the detection process. The attacks are detected by using different deep learning techniques like Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The parameter tuning operation is carried out by using the Hybrid Cat-Particle Swarm Optimization (HCPSO). The simulation results shown in our proposed system detect with better accuracy.

Keywords


Black Hole Attack, Convolutional Neural Network, Mobile Ad-Hoc Networks, Long Short-Term Memory, Hybrid Cat-Particle Swarm Optimization, Grey Hole Attacks.

References





DOI: https://doi.org/10.22247/ijcna%2F2022%2F217705