Review of Intrusion Detection System for Prediction of Cyber Attacks using AI Techniques

Divya Yadav; Prof. Chetan Gupta; Dr. Ritu Shrivastava

doi:10.32628/CSEIT24104128

Authors

Divya Yadav M. Tech. Research Scholar, Department of CSE, SIRTS, Bhopal, India1, Department of CSE, SIRT, Bhopal, India Author
Prof. Chetan Gupta Assistant Professor, Department of CSE, SIRTS, Bhopal, India1, Department of CSE, SIRT, Bhopal, India Author
Dr. Ritu Shrivastava Professor, Department of CSE, SIRTS, Bhopal, India1, Department of CSE, SIRT, Bhopal, India Author

DOI:

https://doi.org/10.32628/CSEIT24104128

Keywords:

IDS, Cyber, AI, NIDS, HIDS, Security

Abstract

The ever-evolving threat landscape of cyber-attacks necessitates continuous advancements in intrusion detection systems (IDS). This paper delves into the application of Artificial Intelligence (AI) techniques to enhance the predictive capabilities of IDS. We explore the limitations of traditional signature-based and anomaly-based IDS approaches and highlight the potential of AI methods like machine learning (ML) and deep learning (DL) for identifying and predicting novel and sophisticated cyber-attacks. By integrating AI into IDS, organizations can bolster their cyber security posture, proactively mitigate threats, and safeguard their critical infrastructure.

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References

S. Ho, S. A. Jufout, K. Dajani and M. Mozumdar, "A Novel Intrusion Detection Model for Detecting Known and Innovative Cyberattacks Using Convolutional Neural Network," in IEEE Open Journal of the Computer Society, vol. 2, pp. 14-25, 2022, doi: 10.1109/OJCS.2021.3050917. DOI: https://doi.org/10.1109/OJCS.2021.3050917

V. K. Navya, J. Adithi, D. Rudrawal, H. Tailor and N. James, "Intrusion Detection System using Deep Neural Networks (DNN)," 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), 2021, pp. 1-6, doi: 10.1109/ICAECA52838.2021.9675513. DOI: https://doi.org/10.1109/ICAECA52838.2021.9675513

Y. A. Farrukh, Z. Ahmad, I. Khan and R. M. Elavarasan, "A Sequential Supervised Machine Learning Approach for Cyber Attack Detection in a Smart Grid System," 2021 North American Power Symposium (NAPS), 2021, pp. 1-6, doi: 10.1109/NAPS52732.2021.9654767. DOI: https://doi.org/10.1109/NAPS52732.2021.9654767

S. Thirimanne, L. Jayawardana, P. Liyanaarachchi and L. Yasakethu, "Comparative Algorithm Analysis for Machine Learning Based Intrusion Detection System," 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), 2021, pp. 191-196, doi: 10.1109/ICIAfS52090.2021.9605814. DOI: https://doi.org/10.1109/ICIAfS52090.2021.9605814

T. T. Nguyen and V. J. Reddi, "Deep Reinforcement Learning for Cyber Security," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2021.3121870. DOI: https://doi.org/10.1109/TNNLS.2021.3121870

W. Xu, J. Jang-Jaccard, A. Singh, Y. Wei and F. Sabrina, "Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset," in IEEE Access, vol. 9, pp. 140136-140146, 2021, doi: 10.1109/ACCESS.2021.3116612. DOI: https://doi.org/10.1109/ACCESS.2021.3116612

K. Cao, J. Zhu, W. Feng, C. Ma, M. Liu and T. Du, "Network Intrusion Detection based on Dense Dilated Convolutions and Attention Mechanism," 2021 International Wireless Communications and Mobile Computing (IWCMC), 2021, pp. 463-468, doi: 10.1109/IWCMC51323.2021.9498652. DOI: https://doi.org/10.1109/IWCMC51323.2021.9498652

I. Ullah and Q. H. Mahmoud, "Design and Development of a Deep Learning-Based Model for Anomaly Detection in IoT Networks," in IEEE Access, vol. 9, pp. 103906-103926, 2021, doi: 10.1109/ACCESS.2021.3094024. DOI: https://doi.org/10.1109/ACCESS.2021.3094024

D. Park, S. Kim, H. Kwon, D. Shin and D. Shin, "Host-Based Intrusion Detection Model Using Siamese Network," in IEEE Access, vol. 9, pp. 76614-76623, 2021, doi: 10.1109/ACCESS.2021.3082160. DOI: https://doi.org/10.1109/ACCESS.2021.3082160

I. Siniosoglou, P. Radoglou-Grammatikis, G. Efstathopoulos, P. Fouliras and P. Sarigiannidis, "A Unified Deep Learning Anomaly Detection and Classification Approach for Smart Grid Environments," in IEEE Transactions on Network and Service Management, vol. 18, no. 2, pp. 1137-1151, June 2021, doi: 10.1109/TNSM.2021.3078381. DOI: https://doi.org/10.1109/TNSM.2021.3078381

Solanki, S., Gupta, C., & Rai, K. (2020). A survey on machine learning based Intrusion Detection System on NSL-KDD dataset. Int. J. Comput. Appl, 176, 36-39. DOI: https://doi.org/10.5120/ijca2020920343

Gupta, C., Sinhal, A., Kamble, R. (2015). An “Enhanced Associative Ant Colony Optimization Technique-based Intrusion Detection System”. Artificial Intelligence and Evolutionary Algorithms in Engineering Systems. Advances in Intelligent Systems and Computing, vol 325. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2135-7_58 DOI: https://doi.org/10.1007/978-81-322-2135-7_58

C Gupta, A Sinhal, R Kamble, “Intrusion detection based on k-means clustering and ant colony optimization: A survey”, International Journal of Computer Applications, 20 Volume 79 – No 6, October 2013. DOI: https://doi.org/10.5120/13747-1555

Jain, T., Gupta, C. (2022). Multi-Agent Intrusion Detection System Using Sparse PSO K-Mean Clustering and Deep Learning. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_10. DOI: https://doi.org/10.1007/978-981-16-6332-1_10

Gupta, C., Kumar, A. & Jain, N.K. An Enhanced Hybrid Intrusion Detection Based on Crow Search Analysis Optimizations and Artificial Neural Network. Wireless Pers Commun 134, 43–68 (2024). https://doi.org/10.1007/s11277-024-10880-3. DOI: https://doi.org/10.1007/s11277-024-10880-3

Solanki, S., Gupta, C., Rai, K., Saxena, M. (2022). An Efficient HIDS System Using Machine Learning Algorithm and Evidence Theory. In: Mathur, G., Bundele, M., Lalwani, M., Paprzycki, M. (eds) Proceedings of 2nd International Conference on Artificial Intelligence: Advances and Applications. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-6332-1_3. DOI: https://doi.org/10.1007/978-981-16-6332-1_3

Dubey, S., Gupta, C. (2024). An Effective Model for Binary and Multi-classification Based on RFE and XGBoost Methods in Intrusion Detection System. In: Roy, N.R., Tanwar, S., Batra, U. (eds) Cyber Security and Digital Forensics. REDCYSEC 2023. Lecture Notes in Networks and Systems, vol 896. Springer, Singapore. https://doi.org/10.1007/978-981-99-9811-1_3. DOI: https://doi.org/10.1007/978-981-99-9811-1_3

Namdev, P., Gupta, C., Dubey, S. (2023). An Improved Intrusion Detection System Using Data Clustering and Support Vector Machine. In: Buyya, R., Misra, S., Leung, YW., Mondal, A. (eds) Proceedings of International Conference on Advanced Communications and Machine Intelligence. MICA 2022. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-99-2768-5_37, DOI: https://doi.org/10.1007/978-981-99-2768-5_37

Gupta, C., Kumar, A., Jain, N.K. (2023). A Detailed Analysis on Intrusion Detection Systems, Datasets, and Challenges. In: Chakraborty, B., Biswas, A., Chakrabarti, A. (eds) Advances in Data Science and Computing Technologies. ADSC 2022. Lecture Notes in Electrical Engineering, vol 1056. Springer, Singapore. https://doi.org/10.1007/978-981-99-3656-4_26. DOI: https://doi.org/10.1007/978-981-99-3656-4_26