Federated reinforcement learning based intrusion detection system using dynamic attention mechanism

Journal of Information Security and Applications - Tập 78 - Trang 103608 - 2023
Sreekanth Vadigi1, Kamalakanta Sethi2, Dinesh Mohanty1, Shom Prasad Das3, Padmalochan Bera1
1Indian Institute of Technology Bhubaneswar, India
2Indian Institute of Information Technology, Sri City, India
3Birla Global University, India

Tài liệu tham khảo

Mbona, 2022, Detecting zero-day intrusion attacks using semi-supervised machine learning approaches, IEEE Access, 10, 69822, 10.1109/ACCESS.2022.3187116 Li, 2014, A new intrusion detection system based on KNN classification algorithm in wireless sensor network, J Electr Comput Eng, 2014 Kuang, 2014, A novel hybrid KPCA and SVM with GA model for intrusion detection, Appl Soft Comput, 18, 178, 10.1016/j.asoc.2014.01.028 Reddy RR, Ramadevi Y, Sunitha KVN. Effective discriminant function for intrusion detection using SVM. In: Proc. int. conf. adv. comput. commun. inform. (ICACCI). 2016, p. 1148–53. Quinlan, 1986, Induction of decision trees, Mach Learn, 1, 81, 10.1007/BF00116251 Ross Quinlan, 1993 Bivens, 2002, Network-based intrusion detection using neural networks, Intell Eng Syst Artif Neural Netw, 12, 579 Lopez-Martin M, Carro B, Sanchez-Esguevillas A. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Syst Appl 141(2020). http://dx.doi.org/10.1016/j.eswa.2019.112963. Lavet, 2018 Nguyen, 2019 Sethi, 2021, Attention based multi-agent intrusion detection systems using reinforcement learning, J Inf Secur Appl, 61, 10.1016/j.jisa.2021.102923 Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan … G, Chintala S. Pytorch: An imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32. Isot cid website. [Online]. Available: https://www.uvic.ca/engineering/ece/isot/datasets/index.phpion. NSL-KDD dataset. [Online], Available: https://www.unb.ca/cic/datasets/nsl.html. Li Q, Wen Z, Wu Z, Hu S, Wang N, Li … Y, He B. A survey on federated learning systems: vision, hype and reality for data privacy and protection. IEEE Trans Knowl Data Eng. McMahan, 2017, Communication-efficient learning of deep networks from decentralized data, 1273 Otterlo Martijn, Wiering Marco. Reinforcement Learning and Markov Decision Processes. Reinforcement Learning: State of the Art. 3-42. http://dx.doi.org/10.1007/978-3-642-27645-3_1. Schaul, 2016 Schaul, 2015 Huber, 1992, 492 Vaswani, 2017, Attention is all you need, Adv Neural Inf Process Syst, 30 Luong, 2015 Bahdanau, 2015 Cheng, 2015 Ingre B, Yadav A, Soni AK. Decision Tree Based Intrusion Detection System for NSL-KDD Dataset. In: Satapathy A, editor. Information and communication technology for intelligent systems (ICTIS 2017) - Volume 2. ICTIS 2017. Smart innovation, systems and technologies, Vol. 84, Springer; http://dx.doi.org/10.1007/978-3-319-63645-0_23. Jing D, Chen H-B. SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset. In: 2019 IEEE 13th international conference on ASIC (ASICON). Chongqing, China; 2019, p. 1–4. http://dx.doi.org/10.1109/ASICON47005.2019.8983598. Meerja AJ, Ashu A, Rajani Kanth A. A Gaussian Naïve Bayes Based Intrusion Detection System. In: Abraham A, Jabbar M, Tiwari S, Jesus I, editors. Proceedings of the 11th international conference on soft computing and pattern recognition (SoCPaR 2019). SoCPaR 2019. Advances in intelligent systems and computing, vol 1182, Cham: Springer, http://dx.doi.org/10.1007/978-3-030-49345-5_16. Pacheco, 2020, Artificial neural networks-based intrusion detection system for internet of things fog nodes, IEEE Access, 8, 73907, 10.1109/ACCESS.2020.2988055 Chen L, Kuang X, Xu A, Suo S, Yang Y. A Novel Network Intrusion Detection System Based on CNN. In: 2020 eighth international conference on advanced cloud and big data (CBD). Taiyuan, China; 2020, p. 243–7. http://dx.doi.org/10.1109/CBD51900.2020.00051. Nayyar S, Arora S, Singh M. Recurrent Neural Network Based Intrusion Detection System. In: 2020 international conference on communication and signal processing (ICCSP). Chennai, India; 2020, p. 0136–40. http://dx.doi.org/10.1109/ICCSP48568.2020.9182099. Alavizadeh, 2022, Deep Q-learning based reinforcement learning approach for network intrusion detection, Computers, 11, 41, 10.3390/computers11030041 Jain, 1988 Blowers, 2014, 55 Farnaaz, 2016, Random forest modelling for network intrusion detection system, Procedia Comput Sci, 89, 10.1016/j.procs.2016.06.047 Injadat, 2018, Bayesian optimization with machine learning algorithms towards anomaly detection, 1 Aljawarneh, 2018, Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model, J Comput Sci, 25, 152, 10.1016/j.jocs.2017.03.006 Wang, 2018, Deep learning-based intrusion detection with adversaries, IEEE Access, 6, 38367, 10.1109/ACCESS.2018.2854599 Qureshi, 2019, A heuristic intrusion detection system for internet-of-things (IoT), Vol. 997 Jiang, 2018, Deep learning based multi-channel intelligent attack detection for data security, IEEE Trans Sustain Comput Hassan, 2020, A hybrid deep learning model for efficient intrusion detection in big data environment, Inform Sci, 513, 386, 10.1016/j.ins.2019.10.069 Wang, 2021, Intrusion detection methods based on integrated deep learning model, Comput Secur, 103, 10.1016/j.cose.2021.102177 Cannady J. Next generation intrusion detection: Autonomous reinforcement learning of network attacks. In: Proceedings of the 23rd national information systems security conference. Baltimore; 2000, p. 1–12. Servin A, Kudenko D. Multi-agent Reinforcement Learning for Intrusion Detection. In: Proceedings of the 5th, 6th and 7th European conference on adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning. 2008, p. 211–23. Xu, 2005, A reinforcement learning approach for host-based intrusion detection using sequences of system calls, Vol. 3644, 995 Konecny J, McMahan HB, Ramage D. Federated Optimization: Distributed Optimization Beyond the Datacenter. ArXiv, abs/1511.03575. Agrawal S, Sarkar S, Aouedi O, Yenduri G, Piamrat K, Bhattacharya S, Maddikunta PK, Gadekallu TR. Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions. ArXiv, abs/2106.09527. Zhuo HH, Feng W, Lin Y, Xu Q, Yang Q. Federated deep reinforcement learning. arXiv preprint arXiv:1901.08277. Wang, 2020, Federated deep reinforcement learning for internet of things with decentralized cooperative edge caching, IEEE Internet Things J, 7, 9441, 10.1109/JIOT.2020.2986803 Wang, 2021, Attention-weighted federated deep reinforcement learning for device-to-device assisted heterogeneous collaborative edge caching, IEEE J Sel Areas Commun, 39, 154, 10.1109/JSAC.2020.3036946 Meena, 2017, A review paper on IDS classification using KDD 99 and NSL KDD dataset in WEKA, 553 Tavallaee, 2009, A detailed analysis of the KDD CUP 99 data set Aldribi A, Traoré I, Moa B, Nwamuo O. Hypervisor-based cloud intrusion detection through online multivariate statistical change tracking. Comput Secur 88. Dong T, Li S, Qiu H, Lu J. An Interpretable Federated Learning-based Network Intrusion Detection Framework. Chen, 2020, Intrusion detection for wireless edge networks based on federated learning, IEEE Access, 8, 217463, 10.1109/ACCESS.2020.3041793 Cetin B, Lazar A, Kim J, Sim A, Wu K. Federated Wireless Network Intrusion Detection. In: 2019 IEEE international conference on big data (Big Data). Los Angeles, CA, USA; 2019, p. 6004–6. http://dx.doi.org/10.1109/BigData47090.2019.9005507. Li, 2020, Distributed network intrusion detection system in satellite-terrestrial integrated networks using federated learning, IEEE Access, 8, 214852, 10.1109/ACCESS.2020.3041641