Volume 14 , Issue 1 , PP: 96-113, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
S. Phani Praveen 1 * , Anuradha Chokka 2 , Pappula Sarala 3 , Rajeswari Nakka 4 , Suresh Babu Chandolu 5 , V. Esther Jyothi 6
Doi: https://doi.org/10.54216/JCIM.140107
Ordinary defence components like rule-based firewalls and mark based detection are not staying aware of the always expanding intricacy and frequency of cyber security dangers. The reason for this work is to explore the way that deep reinforcement learning (DRL), a subfield of artificial intelligence famous for its viability in handling testing decision-production situations, may be utilized to improve cyber security conventions. To mimic and balance threatening cyber-attacks, we present a system that utilizes deep reinforcement learning (DRL). We propose a specialist based model that can learn and adjust ceaselessly in powerful network security situations. In light of the present status of the network and the rewards it gets for its decisions, the specialist concludes what the best game-plans are. Specifically, we utilize the policy gradient (PG)- based double deep Q-network (DDQN) model and trial on three different datasets: NSL-KDD, CIC-IDS, and AWID. Our review demonstrates the way that DRL can really further develop the detection after-effects of cyber-attacks. Utilizing the policy gradient DDQN model on different datasets, we find prominent upgrades in cyber security conventions. Specific boundary modifications upgrade the viability of our philosophy much more, displaying empowering results on different datasets. This exploration features the potential of deep reinforcement learning (DRL) as a successful instrument in the field of cyber security. Our examination progresses detection techniques and gives a versatile arrangement that can be applied to an assortment of cyber security worries by giving areas of strength for a to demonstrating and relieving cyber dangers.
Deep reinforcement learning , Detection , Cyber-attacks , Network security , Double deep Q-network , Policy gradient.
[1.] Alavizadeh, H., Alavizadeh, H., & Jang-Jaccard, J. (2022). Deep Q-learning based reinforcement learning approach for network intrusion detection. Computers, 11(3), 41.
[2.] Basnet, R. B., Shash, R., Johnson, C., Walgren, L., & Doleck, T. (2019). Towards detecting and classifying network intrusion traffic using deep learning frameworks. Journal of Internet Services and Information Security, 9(4), 1–7.
[3.] Bhattacharya, A., Ramachandran, T., Banik, S., Dowling, C. P., & Bopardikar, S. D. (2020). Automated adversary emulation for cyber-physical systems via reinforcement learning. In Proceedings of the 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) (pp. 1–6). IEEE.
[4.] Praveen, S. P., Sindhura, S., Srinivasu, P. N., & Ahmed, S. (2023, September). Combining CNNs and Bi-LSTMs for Enhanced Network Intrusion Detection: A Deep Learning Approach. In 2023 3rd International Conference on Computing and Information Technology (ICCIT) (pp. 261-268). IEEE.
[5.] Mahmoud M. Ismail, Ahmed A. Metwaly. "Enhancing Wireless Ad-Hoc Network Security by Mitigating Distributed Denial-of-Service (DDoS) Attacks." Full Length Article, Vol. 8, No. 2, 2024 ,PP. 46-52 (Doi : https://doi.org/10.54216/IJWAC.080205)
[6.] Dong, S., Xia, Y., & Peng, T. (2021). Network abnormal traffic detection model based on semi-supervised deep reinforcement learning. IEEE Transactions on Network and Service Management, 18(4), 4197–4212.
[7.] Dutta, A., Chatterjee, S., Bhattacharya, A., & Halappanavar, M. (2023). Deep reinforcement learning for cyber system defense under dynamic adversarial uncertainties. arXiv preprint arXiv:2302.01595.
[8.] Franco, M. F., Sula, E., Huertas, A., Scheid, E. J., Granville, L. Z., & Stiller, B. (2022). SecRiskAI: A machine learning-based approach for cybersecurity risk prediction in businesses. In Proceedings of the 2022 IEEE 24th Conference on Business Informatics (CBI) (Vol. 1, pp. 1–10). IEEE.
[9.] Haque, N. I., Shahriar, M. H., Dastgir, M. G., Debnath, A., Parvez, I., Sarwat, A., & Rahman, M. A. (2020). Machine learning in generation, detection, and mitigation of cyberattacks in smart grid: A survey. arXiv preprint arXiv:2010.00661.
[10.] Huang, Y., Huang, L., & Zhu, Q. (2022). Reinforcement learning for feedback-enabled cyber resilience. Annual Review of Control, 53, 273–295.
[11.] Khaw, Y. M., Jahromi, A. A., Arani, M. F., Sanner, S., Kundur, D., & Kassouf, M. (2020). A deep learning-based cyberattack detection system for transmission protective relays. IEEE Transactions on Smart Grid, 12(3), 2554-2565.
[12.] Landen, M., Chung, K., Ike, M., Mackay, S., Watson, J. P., & Lee, W. (2022). DRAGON: Deep reinforcement learning for autonomous grid operation and attack detection. In Proceedings of the 38th Annual Computer Security Applications Conference (pp. 13–27).
[13.] Bikku, T., Chandolu, S. B., Praveen, S. P., Tirumalasetti, N. R., Swathi, K., & Sirisha, U. (2024). Enhancing Real-Time Malware Analysis with Quantum Neural Networks. Journal of Intelligent Systems and Internet of Things, 12(1), 57-7.
[14.] Meier, R., Lavrenovs, A., Heinäaro, K., Gambazzi, L., & Lenders, V. (2021). Towards an AI-powered player in cyber defence exercises. In Proceedings of the 2021 13th International Conference on Cyber Conflict (CyCon) (pp. 309–326). IEEE.
[15.] Mahmoud A. Zaher, Mohmaed A. Labib, Artificial Flora Optimization Algorithm with Functional Link Neural Network for DoS Attack Classification in WSN, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 4 , No. 1 , (2022) : 08-18 (Doi : https://doi.org/10.54216/IJWAC.040101)
[16.] Mesadieu, F., Torre, D., & Chennameneni, A. (2024). Leveraging deep reinforcement learning technique for intrusion detection in SCADA infrastructure. IEEE Access.
[17.] Reddy, A. S., Praveen, S. P., Ramudu, G. B., Anish, A. B., Mahadev, A., & Swapna, D. (2023, January). A network monitoring model based on convolutional neural networks for unbalanced network activity. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1267-1274). IEEE.
[18.] Paidipati, K. K., Kurangi, C., Uthayakumar, J., Padmanayaki, S., Pradeepa, D., & Nithinsha, S. (2024). Ensemble of deep reinforcement learning with optimization model for DDoS attack detection and classification in cloud based software defined networks. Multimedia Tools and Applications, 83(11), 32367-32385.
[19.] Piplai, A., Anoruo, M., Fasaye, K., Joshi, A., Finin, T., & Ridley, A. (2022). Knowledge guided Two-player Reinforcement Learning for Cyber Attacks and Defenses. In Proceedings of the International Conference on Machine Learning and Applications, Nassau, Bahamas.
[20.] Radoglou-Grammatikis, P., Rompolos, K., Sarigiannidis, P., Argyriou, V., Lagkas, T., Sarigiannidis, A., ... & Wan, S. (2021). Modeling, detecting, and mitigating threats against industrial healthcare systems: A combined software defined networking and reinforcement learning approach. IEEE Transactions on Industrial Informatics, 18(3), 2041-2052.
[21.] Randhawa, R. H., Aslam, N., Alauthman, M., Khalid, M., & Rafiq, H. (2023). Deep reinforcement learning based evasion generative adversarial network for botnet detection. SSRN.
[22.] Ren, K., Zeng, Y., Cao, Z., & Zhang, Y. (2022). ID-RDRL: A deep reinforcement learning-based feature selection intrusion detection model. Scientific Reports, 12(1), 15370.
[23.] Salam, A., Ullah, F., Amin, F., & Abrar, M. (2023). Deep learning techniques for web-based attack detection in industry 5.0: A novel approach. Technologies, 11(4), 107.
[24.] Sarker, I. H. (2022). Multi-aspects AI-based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview. Security and Privacy, 6, e295.
[25.] Selim, A., Zhao, J., Ding, F., Miao, F., & Park, S. Y. (2023). Adaptive deep reinforcement learning algorithm for distribution system cyber attack defense with high penetration of DERs. IEEE Transactions on Smart Grid.
[26.] Mahmoud A. Zaher, Nabil M. Eldakhly, Cyber Attack Detection in Wireless Adhoc Network using Artificial Intelligence, Journal of International Journal of Wireless and Ad Hoc Communication, Vol. 6 , No. 2 , (2023) : 18-33 (Doi : https://doi.org/10.54216/IJWAC.060202).
[27.] Talukder, M. A., Hasan, K. F., Islam, M. M., Uddin, M. A., Akhter, A., Yousuf, M. A., Alharbi, F., & Moni, M. A. (2023). A dependable hybrid machine learning model for network intrusion detection. Journal of Information Security Applications, 72, 103405.
[28.] Tharewal, S., Ashfaque, M. W., Banu, S. S., Uma, P., Hassen, S. M., & Shabaz, M. (2022). Intrusion detection system for industrial Internet of Things based on deep reinforcement learning. Wireless Communications and Mobile Computing, 2022, 1-8.
[29.] Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525-41550.
[30.] Wu, C., Pan, W., Staa, R., Liu, J., Sun, G., & Wu, L. (2023). Deep reinforcement learning control approach to mitigating actuator attacks. Automatica, 152, 110999.
[31.] S. Phani Praveen , Thulasi Bikku, P. Muthukumar, K. Sandeep, Jampani Chandra Sekhar, V. Krishna Pratap. (2024). Enhanced Intrusion Detection Using Stacked FT-Transformer Architecture. Journal of , 13 ( 2 ), 19-29 (Doi : https://doi.org/10.54216/JCIM.130202)
[32.] Biyyapu, N., Veerapaneni, E. J., Surapaneni, P. P., Vellela, S. S., & Vatambeti, R. (2024). Designing a modified feature aggregation model with hybrid sampling techniques for network intrusion detection. Cluster Computing, 1-19.
[33.] Aruna, R., Kushwah, V. S., Praveen, S. P., Pradhan, R., Chinchawade, A. J., Asaad, R. R., & Kumar, R. L. (2024). Coalescing novel QoS routing with fault tolerance for improving QoS parameters in wireless Ad-Hoc network using craft protocol. Wireless Networks, 30(2), 711-735.
Jyothi, V. E., Kumar, D. L. S., Thati, B., Tondepu, Y., Pratap, V. K., & Praveen, S. P. (2022, December). Secure data access management for cyber threats using artificial intelligence. In 2022 6th International Conference on Electronics, Communication and Aerospace Technology
