Abstract
As of late, AI-based anomaly detection has found a new interest, as the number and complexity of new breaches keep on improvising, subsequently, newer approaches to evolve and best deal with the attacks are fundamental. We propose the artificial neural networks to devise a novel cyber intrusion detection method. While the ANNs are popularly trained by the back propagation and genetic algorithm, we propose the particle swarm optimisation method to help resolve issues like slow convergence rate and easily getting trapped in local minima which arise with back Propagation and genetic algorithm. The proposed approach utilises the standard NSL-KDD data-set used in the field of anomaly detection method. The test results show that our strategy performs better than a portion of the current procedures including ANN-BP, ANN- GA, etc. and give accuracy in the range of 97–99%.
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References
Shilpashree, S., et al. (2020). Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection in Wireless Network Using KDDCUP’99 and NSLKDD Datasets. International Journal of Advanced Science and Technology, 29(3), 15037-15052–15037-15052
Pu, G., Wang, L., Shen, J., & Dong, F. (2021). A hybrid unsupervised clustering-based anomaly detection method. Tsinghua Science and Technology, 26(2), 146–153.
Chandre, P. R., Mahalle, P. N., & Shinde, G. R. (2018). Machine learning based novel approach for intrusion detection and prevention system: A tool based verification. In IEEE Global Conference on Wireless Computing and Networking (GCWCN) (pp. 135–140). India: Lonavala.
Fischer-Hübner, S. (n.d.). Intrusion Detection (IDS). Karlstad University Computer Science. www.cs.kau.se/cs/education/courses/dvgc04/07p5/slides/Intrusion%20Detection%20(IDS).pdf
Zhang, X., Niyaz, Q., Jahan, F., & Sun, W. (2020). Early detection of host-based intrusions in linux environment. In IEEE International Conference on Electro Information Technology (EIT) (pp. 475–479). Chicago: IL, USA.
Malek, Z. S., Trivedi, B., & Shah, A. (2020). User behavior pattern -signature based intrusion detection. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) (pp. 549–552). London: United Kingdom.
Anand Sukumar, J. V., Pranav I., Neetish, M. & Narayanan, J. (2018). Network intrusion detection using improved genetic k-means algorithm. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (pp. 2441–2446). Bangalore.
Halimaa, A., & Sundarakantham, K. (2019). Machine learning based intrusion detection system. In 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 916–920). India: Tirunelveli.
Johari, R., Kalra, S., Dahiya, S., & Gupta, K. (2021). S2NOW: Secure social network ontology using whatsApp. Security and Communication Networks, 2021, Article ID 7940103, 21 pages.
Dahiya, S., & Sharma, R. (2018). Comparative study of popular cryptographic techniques. In 2018 Second World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4) (pp. 36–43). London.
KDD Cup 1999 Data, kdd.ics.uci.edu/databases/kddcup99/kddcup99.html
Kaliappan, J. (2015). Intrusion detection using artificial neural networks with best set of features. International Arab Journal of Information Technology.
Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1–6). Ottawa, ON.
Hamid, Y., Ranganathan, B., Journaux, L., & Sugumaran, M. (2018). Benchmark datasets for network intrusion detection: A review. International Journal of Network Security.
McHugh, J. (2000). Testing intrusion detection systems: a critique of the 1998 and 1999 darpa intrusion detection system evaluations as performed by lincoln laboratory. ACM Transactions on Information and System Security, 3(4), 262–294.
Taher, K. A., Jisan, B. M., & Rahman, M. M. (2019). Network intrusion detection using supervised machine learning technique with feature selection. In 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST), pp. 643–646.
NSL-KDD-Datasets-Research-Canadian Institute for Cybersecurity-UNB. (n.d.). University of New Brunswick www.unb.ca/cic/datasets/nsl.html
Latah, M., & Toker, L. (2018). Towards an efficient anomaly-based intrusion detection for software-defined networks. IET Networks, 2(7), no. 6, pp. 453–59. arXiv.org.
Nagappan, K., Kanmani, S., & Uthariaraj, V. (2013). Improving fault prediction using ANN-PSO in object oriented systems. International Journal of Computer Applications.
Khraisat, A., Gondal, I., Vamplew, P., et al. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecur, 2, 20.
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Dahiya, S., Soni, P., Nadappattel, H.S., Fraz, M. (2022). A Hybrid Approach of ANN-PSO Technique for Anomaly Detection. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_61
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DOI: https://doi.org/10.1007/978-981-16-3346-1_61
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