Chained Hash Table-Based Filtering Model for Detection of Flooding and Dos Attack in Manet
Keywords:
Denial-of-Service (DoS), Mobile Ad Hoc Network (MANET), Chinese Remainder Theory based Digital Signature Algorithm (CRT-DSA), Boltzmann Gibbs Trapezoidal Entropy based Fuzzy (BGTE-Fuzzy) Algorithm, Correlation Coefficient based Galactic Swarm Optimization (CC-GSO), Soft kernel Swish based Recurrent Neural Network (SS-RNN), Particle Swarm Optimization (PSO)Abstract
Mobile Ad Hoc Network (MANET), which connects mobile devices to all devices in the network where security is an essential task caused by Malicious Nodes (MNs), is a wireless communication technology. One of the crucial attacks that aim to exhaust network resources by flooding it with numerous fake packets as well as messages is the flooding attack. Hence, to detect flooding attacks, many models were developed. However, the models did not detect the sequences of flooding attacks. Therefore, this work proposes a soft kernel Swish-based Recurrent Neural Network (SS-RNN)-based sequential flooding attack detection model in MANET. Primarily, the nodes are initialized. Thereafter, the signature is created for the packet information utilizing the Chinese Remainder Theory-based Digital Signature Algorithm (CRT-DSA). Afterward, the packets are transmitted to nodes for connection establishment. The signed packet is verified, and to eliminate the attacked packets, the legitimate user’s packets are filtered utilizing Boltzmann Gibbs Trapezoidal Entropy based Fuzzy (BGTE-Fuzzy). By utilizing XNOR-HAVAL, the packet information is hashed and added into the chaining grounded on time series to identify flooding attacks, namely HELLO, Internet Control Message Protocol (ICMP), Synchronize an Acknowledgement (SYN-ACK), Acknowledgement (ACK), and Data flooding. After that, using SS-RNN, Denial-of-Service (DoS) attacks are detected. Lastly, for data transmission, the optimal path is created utilizing Correlation Coefficient-based Galactic Swarm Optimization (CC-GSO). To prevent security in MANET, the developed model classifies attacks with an accuracy of 98.41%.
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Abaeian, V., Abdullah, A., Pillai, T., & Cai, L. Z. (2015). Intrusion detection forecasting using time series for improving cyber defence. International Journal of Intelligent Systems and Applications in Engineering, 3(1), 28-33.
Alam, T. (2020). Efficient and secure data transmission approach in cloud-MANET-IoT integrated framework. Journal of Telecommunication, Electronic and Computer Engineering, 12(1), 1-10.
Aytekin, T. (2018). Reservoir Sampling Based Streaming Method for Large Scale Collaborative Filtering. International Journal of Intelligent Systems and Applications in Engineering, 6(3), 191-196.
Banga, S., Arora, H., Sankhla, S., Sharma, G., & Jain, B. (2021). Performance Analysis of Hello Flood Attack in WSN. Proceedings of International Conference on Communication and Computational Technologies, 335–342. https://doi.org/10.1007/978-981-15-5077-5_30
Bouyeddou, B., Kadri, B., Harrou, F., & Sun, Y. (2020). DDOS-attacks detection using an efficient measurement-based statistical mechanism.Engineering Science and Technology, an International Journal, 23(4), 870–878. https://doi.org/10.1016/j.jestch.2020.05.002
Dilipkumar, S., &Durairaj, M. (2023).Epilson Swarm Optimized Cluster Gradient and deep belief classifier for multi-attack intrusion detection in MANET.Journal of Ambient Intelligence and Humanized Computing, 14(3), 1445–1460. https://doi.org/10.1007/s12652-021-03169-x
Elhigazi, A., Razak, S. A., Hamdan, M., Mohammed, B., Abaker, I., &Elsafi, A. (2020). Authentication Flooding DOS Attack Detection and Prevention in 802.11.IEEE Student Conference on Research and Development, 325–329. https://doi.org/10.1109/SCOReD50371.2020.9250990
Farahani, G. (2021). Black Hole Attack Detection Using K-Nearest Neighbor Algorithm and Reputation Calculation in Mobile Ad Hoc Networks.Security and Communication Networks, 2021, 1–15. https://doi.org/10.1155/2021/8814141
Gadzama, E., &Saidu, I. (2022). Developing an Efficient Hybridized Routing Protocol for Mitigation of Flooding Attacks in Mobile Ad-Hoc Networks.Nigerian Defence Academy Journal of Military Science and Interdisciplinary Studies, 1(1), 1–8.
Gautam, D., &Tokekar, V. (2020). A novel approach for detecting DDoS attack in MANET.Materials Today: Proceedings, 29, 674–677. https://doi.org/10.1016/j.matpr.2020.07.332
Hai, T., Zhou, J., Lu, Y., Jawawi, D., Wang, D., Onyema, E. M., &Biamba, C. (2023). Enhanced security using multiple paths routine scheme in cloud-MANETs.Journal of Cloud Computing, 12(1), 1–23. https://doi.org/10.1186/s13677-023-00443-5
Işık, Ş., Özkan, K., Gerek, Ö. N., & Doğan, M. (2016). A new subspace based solution to background modelling and change detection. International Journal of Intelligent Systems and Applications in Engineering, 4(Special Issue-1), 82-86.
Islabudeen, M., & Devi, M. K. K. (2020). A Smart Approach for Intrusion Detection and Prevention System in Mobile Ad Hoc Networks Against Security Attacks. In Wireless Personal Communications (Vol. 112, Issue 1). Springer US.https://doi.org/10.1007/s11277-019-07022-5
Kamoji, S., Kalla, M., & Shamshi, I. (2022). A Framework for Flood Extent Mapping using CNN Transfer Learning. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 150-157.
Karthigha, M., Latha, L., &Sripriyan, K. (2020). A Comprehensive Survey of Routing Attacks in Wireless Mobile Ad hoc Networks. Proceedings of the 5th International Conference on Inventive Computation Technologies, 396–402. https://doi.org/10.1109/ICICT48043.2020.9112588
Khalfaoui, H., Farchane, A., & Safi, S. (2023). An Overview of the Security Improvements of Artificial Intelligence in MANET.International Conference on Cybersecurity, Cybercrimes, and Smart Emerging Technologies, 4, 135–146. https://doi.org/10.1007/978-3-031-21101-0_11
Kshirsagar, D., & Kumar, S. (2022). A feature reduction based reflected and exploited DDoS attacks detection system. Journal of Ambient Intelligence and Humanized Computing, 13(1), 393–405. https://doi.org/10.1007/s12652-021-02907-5
Mittal, M., Kumar, K., &Behal, S. (2023). Deep learning approaches for detecting DDoS attacks: a systematic review. Soft Computing, 27(18), 13039–13075. https://doi.org/10.1007/s00500-021-06608-1
Nandi, M., &Kannan, A. (2022). An Optimized and Hybrid Energy Aware Routing Model for Effective Detection of Flooding Attacks in a Manet Environment. Wireless Personal Communications, 127(3), 2515–2533.https://doi.org/10.1007/s11277-021-09079-7
Narmadha, A. S., Maheswari, S., &Deepa, S. N. (2023). Watchdog malicious node detection and isolation using deep learning for secured communication in MANET.Automatika, 64(4), 996–1009.https://doi.org/10.1080/00051144.2023.2241766
Nishanth, N., &Mujeeb, A. (2021). Modeling and Detection of Flooding-Based Denial-of-Service Attack in Wireless Ad Hoc Network Using Bayesian Inference. IEEE Systems Journal, 15(1), 17–26. https://doi.org/10.1109/JSYST.2020.2984797
Qanbar, M. M., & Tasdemir, S. (2019). Detection of malaria diseases with residual attention network. International Journal of Intelligent Systems and Applications in Engineering, 7(4), 238-244.
Rahouti, M., Xiong, K., Ghani, N., &Shaikh, F. (2021).SYNGuard: Dynamic threshold-based SYN flood attack detection and mitigation in software-defined networks. IET Networks, 10(2), 76–87. https://doi.org/10.1049/ntw2.12009
Ramesh, S., Yaashuwanth, C., &Muthukrishnan, B. A. (2020). Machine learning approach for secure communication in wireless video sensor networks against denial-of-service attacks. International Journal of Communication Systems, 33(12), 1–12. https://doi.org/10.1002/dac.4073
Ravi, N., Shalinie, S. M., &Theres, D. D. J. (2020). BALANCE: Link Flooding Attack Detection and Mitigation via Hybrid-SDN. IEEE Transactions on Network and Service Management, 17(3), 1715–1729. https://doi.org/10.1109/TNSM.2020.2997734
Salunke, K., &Ragavendran, U. (2021). Shield Techniques for Application Layer DDoS Attack in MANET: A Methodological Review. Wireless Personal Communications, 120(4), 2773–2799.https://doi.org/10.1007/s11277-021-08556-3
Sbai, O., &Elboukhari, M. (2022). Deep learning intrusion detection system for mobile ad hoc networks against flooding attacks.IAES International Journal of Artificial Intelligence, 11(3), 878–885. https://doi.org/10.11591/ijai.v11.i3.pp878-885
Shafi, S., Mounika, S., &Velliangiri, S. (2022). Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET. Procedia Computer Science, 218, 2309–2318. https://doi.org/10.1016/j.procs.2023.01.206
Shafi, S., Mounika, S., &Velliangiri, S. (2023). Machine Learning and Trust Based AODV Routing Protocol to Mitigate Flooding and Blackhole Attacks in MANET. Procedia Computer Science, 218, 2309–2318. https://doi.org/10.1016/j.procs.2023.01.206
Srinivas, T. A. S., &Manivannan, S. S. (2020). Prevention of Hello Flood Attack in IoT using combination of Deep Learning with Improved Rider Optimization Algorithm.Computer Communications, 163, 162–175. https://doi.org/10.1016/j.comcom.2020.03.031
Talukdar, M. I., Hassan, R., Hossen, M. S., Ahmad, K., Qamar, F., & Ahmed, A. S. (2021). Performance Improvements of AODV by Black Hole Attack Detection Using IDS and Digital Signature.Wireless Communications and Mobile Computing, 2021, 1–13. https://doi.org/10.1155/2021/6693316
Veeraiah, N., & Krishna, B. T. (2022). An approach for optimal-secure multi-path routing and intrusion detection in MANET. Evolutionary Intelligence, 15(2), 1313–1327. https://doi.org/10.1007/s12065-020-00388-7
Yamini, K. A. P., Stephy, J., Suthendran, K., & Ravi, V. (2022). Improving routing disruption attack detection in MANETs using efficient trust establishment.Transactions on Emerging Telecommunications Technologies, 33(5), 1–16.https://doi.org/10.1002/ett.4446
Zalte, S.., Kamat, R.., &Ghorpade, V.. (2020). Mitigation of DDoS Attack in MANET.International Journal of Engineering and Advanced Technology, 9(6), 410–413. https://doi.org/10.35940/ijeat.e95400.089620
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