A Novel Approach of DDOS Attack Classification with Genetic Algorithm-optimized Spiking Neural Network
Автор: Anuradha Pawar, Nidhi Tiwari
Журнал: International Journal of Computer Network and Information Security @ijcnis
Статья в выпуске: 2 vol.16, 2024 года.
Бесплатный доступ
Spiking Neural Network (SNN) use spiking neurons that transmit information through discrete spikes, similar to the way biological neurons communicate through action potentials. This unique property of SNNs makes them suitable for applications that require real-time processing and low power consumption. This paper proposes a new method for detecting DDoS attacks using a spiking neural network (SNN) with a distance-based rate coding mechanism and optimizing the SNN using a genetic algorithm (GA). The proposed GA-SNN approach achieved a remarkable accuracy rate of 99.98% in detecting DDoS attacks, outperforming existing state-of-the-art methods. The GA optimization approach helps to overcome the challenges of setting the initial weights and biases in the SNN, and the distance-based rate coding mechanism enhances the accuracy of the SNN in detecting DDoS attacks. Additionally, the proposed approach is designed to be computationally efficient, which is essential for practical implementation in real-time systems. Overall, the proposed GA-SNN approach is a promising solution for accurate and efficient detection of DDoS attacks in network security applications.
CSE-CIC-IDS-2018, DDoS, Genetic Algorithm, GA-SNN, Spiking Neural Network
Короткий адрес: https://sciup.org/15019276
IDR: 15019276 | DOI: 10.5815/ijcnis.2024.02.09
Список литературы A Novel Approach of DDOS Attack Classification with Genetic Algorithm-optimized Spiking Neural Network
- A. Tavanaei, M. Ghodrati, S. R. Kheradpisheh, T. Masquelier, and A. Maida, “Deep Learning in Spiking Neural Networks”, Neural networks, Vol.111, pp.47-63, 2019. https://doi.org/10.1016/j.neunet.2018.12.002
- E. O. Neftci, H. Mostafa, and F. Zenke, “Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks”, IEEE Signal Processing Magazine, Vol.36, No.6, pp.51-63, 2019. https://doi.org/10.1109/MSP.2019.2931595
- M. Lopez-Martin, B. Carro, and A. Sanchez-Esguevillas, “Application of Deep Reinforcement Learning to Intrusion Detection for Supervised Problems”, Expert Systems with Applications, Vol.141, pp.112963, 2020. https://doi.org/10.1016/j.eswa.2019.112963
- Y. Wu, L. Deng, G. Li, J. Zhu, and L. Shi, “Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks”, Frontiers in Neuroscience, Vol.12, pp.331, 2018. https://doi.org/10.3389/fnins.2018.00331
- S. R. Kheradpisheh, M. Ganjtabesh, S. J. Thorpe, and T. Masquelier, “STDP-Based Spiking Deep Convolutional Neural Networks for Object Recognition”, Neural Networks, Vol.99, pp.56-67, 2018. https://doi.org/10.1016/j.neunet.2017.12.005
- S. Zhou, and X. Li, “Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection”, In 25th International Conference on Pattern Recognition (ICPR), pp.8148-8155, 2021. https://doi.org/10.1109/ICPR48806.2021.9412580
- I. Benmessahel, K. Xie, M. Chellal, and T. Semong, “A New Evolutionary Neural Networks Based on Intrusion Detection Systems using Locust Swarm Optimization”, Evolutionary Intelligence, Vol.12, pp.131-146, 2019. https://doi.org/10.1007/s12065-019-00199-5
- A. R. Zarzoor, N. A. S. Al-Jamali, and D. A. A. Qader, “Intrusion Detection Method for Internet of Things Based on the Spiking Neural Network and Decision Tree Method”, International Journal of Electrical and Computer Engineering, Vol.13, No.2, pp.2278, 2023. https://doi.org/10.11591/ijece.v13i2.pp2278-2288
- W. Ye, Y. Chen, and Y. Liu, “The Implementation and Optimization of Neuromorphic Hardware for Supporting Spiking Neural Networks with MLP and CNN Topologies”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022. https://doi.org/10.1109/TCAD.2022.3179246
- M.R. Hadi and A.S. Mohammed, “A Novel Approach to Network Intrusion Detection System Using Deep Learning For SDN”: Futuristic approach. arXiv preprint arXiv:2208.02094, 2022. https://doi.org/10.48550/arXiv.2208.02094
- T.A. Tang, D. McLernon, L. Mhamdi, S.A.R. Zaidi, and M. Ghogho, “Intrusion detection in SDN-based networks: Deep recurrent neural network approach”, Deep Learning Applications for Cyber Security, pp.175-195, 2019. https://doi.org/10.1007/978-3-030-13057-2_8
- C. Yin, Y. Zhu, S. Liu, J. Fei, and H. Zhang, “Enhancing Network Intrusion Detection Classifiers using Supervised Adversarial Training”. The Journal of Supercomputing, Vol.76, No.9, pp.6690-6719, 2020. https://doi.org/10.1007/s11227-019-03092-1
- M. Arshi, M.D. Nasreen, and K. Madhavi, “A survey of DDoS attacks using machine learning techniques”, In E3S Web of Conferences, EDP Sciences, Vol.184, p. 01052, 2020. https://doi.org/10.1051/e3sconf/202018401052
- B. Selvakumar, and K. Muneeswaran, “Firefly algorithm based feature selection for network intrusion detection”, Computers & Security, Vol.81, pp.148-155, 2022. https://doi.org/10.1016/j.cose.2018.11.005
- M. Najafimehr, S. Zarifzadeh, and S. Mostafavi, “A hybrid machine learning approach for detecting unprecedented DDoS attacks”, The Journal of Supercomputing, Vol.78, No.6, pp.8106-8136, 2022. https://doi.org/10.1007/s11227-021-04253-x
- S. Seth, K. K. Chahal, and G. Singh, “A Novel Ensemble Framework for an Intelligent Intrusion Detection System”, IEEE Access, Vol.9, pp.138451–138467, 2021. https://doi.org/10.1109/ACCESS.2021.3116219
- U. Uhongora, R. Mulinde, Y.W. Law, and J. Slay, “Deep-learning-based Intrusion Detection for Software-defined Networking Space Systems”, In European Conference on Cyber Warfare and Security, Vol. 22, No. 1, pp. 639-647, 2023. https://doi.org/10.34190/eccws.22.1.1085
- P. Lin, K. Ye, and C. Z. Xu, “Dynamic Network Anomaly Detection System by Using Deep Learning Techniques”, In Cloud Computing–CLOUD 2019: 12th International Conference, Held as Part of the Services Conference Federation, SCF 2019, San Diego, CA, USA, Proceedings 12, Springer International Publishing, pp.161-176, 2019. https://doi.org/10.1007/978-3-030-23502-4_12
- B. I. Farhan, and A. D. Jasim, “Performance Analysis of Intrusion Detection for Deep Learning Model Based on CSE-CIC-IDS2018 Dataset”, Indonesian Journal of Electrical Engineering and Computer Science, Vol.26, No.2, pp.1165-1172, 2022. https://doi.org/10.11591/ijeecs.v26.i2.pp1165-1172
- A. Makuvaza, D. S. Jat, and A. M. Gamundani, “Deep Neural Network (DNN) Solution for Real-Time Detection of Distributed Denial of Service (DDoS) Attacks in Software Defined Networks (SDNs)”, SN Computer Science, Vol.2, pp.1-10, 2021. https://doi.org/10.1007/s42979-021-00467-1
- J. L. Lobo, J. Del Ser, A. Bifet, and N. Kasabov, “Spiking Neural Networks and Online Learning: An Overview and Perspectives”, Neural Networks, Vol.121, pp.88-100, 2020. https://doi.org/10.1016/j.neunet.2019.09.004
