Abstract
The rapid development in Internet has brought great convenience in our daily life, as well as cyber criminals focus shifted from real to virtual life as well. These security problems threaten user’s privacy and also increasing prominently because hackers launch attacks by injecting malicious software in to the network and disturbs normal flow of network activities. Traditional Artificial Intelligence specifically Machine Learning Algorithms is no longer effective in detecting new attack patterns and unknown abnormal behavior in the network traffic. The anomaly detection process helps in providing the report of malicious activities and gives better results. To detect abnormal traffic in network, we need a strong robust deep learning abnormal traffic detection framework. In this paper, different machine learning algorithms were studied in detecting the anomalies and cyber-attacks in network and to identify the problem in existing solutions.
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References
Liu Z, Thapa N, Shaver A, Roy K, Yuan X, Khorsandroo S (2020) Anomaly detection on IoT network intrusion using machine learning. In: 2020 International conference on artificial intelligence, big data, computing and data communication systems (icABCD), pp 1–5. IEEE
Zhou X, Liang W, Shimizu S, Ma J, Jin Q (2020) Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems. IEEE Trans Industr Inf 17(8):5790–5798
Demertzis K, Iliadis L, Bougoudis I (2020) Gryphon: a semi-supervised anomaly detection system based on one-class evolving spiking neural network. Neural Comput Appl 32:4303–4314
Karimipour H, Leung H (2020) Relaxation-based anomaly detection in cyber-physical systems using ensemble Kalman filter. IET Cyber-Phys Syst Theory Appl 5(1):49–58
Schneider P, Böttinger K (2018) High-performance unsupervised anomaly detection for cyber-physical system networks. In: Proceedings on cyber-physical systems security and privacy, pp 1–12
Siddiqi MA, Pak W (2021) An agile approach to identify single and hybrid normalization for enhancing machine learning-based network intrusion detection. IEEE Access 9:137494–137513
Ullah I, Mahmoud QH (2020) A two-level flow-based anomalous activity detection system for IoT networks. Electronics 9(3):530
Ullah I, Mahmoud QH (2020) A technique for generating a botnet dataset for anomalous activity detection in IoT networks. In: 2020 IEEE transactions on Systems, Man, and Cybernetics (SMC), pp 134–140. IEEE
Sarker IH (2021) Cyberlearning: effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks. Internet Things 14:100393
Bertoli GDC et al (2021) An end-to-end framework for machine learning-based network intrusion detection system. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3101188
Kavousi-Fard A, Su W, Jin T (2020) A machine-learning-based cyber-attack detection model for wireless sensor networks in microgrids. IEEE Trans Industr Inf 17(1):650–658
Siddiqi MA, Pak W (2021) An agile approach to identify single and hybrid normalization for enhancing machine learning-based network intrusion detection. IEEE Access 9(2021):137494–137513
Shafiq M, Tian Z, Sun Y, Du X, Guizani M (2020) Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city. Futur Gener Comput Syst 107:433–442
Singh VK, Govindarasu M (2021) A cyber-physical anomaly detection for wide-area protection using machine learning. IEEE Trans Smart Grid 12(4):3514–3526
Dong S, Xia Y, Peng T (2021) Network abnormal traffic detection model based on semi-supervised deep reinforcement learning. IEEE Trans Netw Serv Manage 18(4):4197–4212
Schneider P, Böttinger B (2018) High-performance unsupervised anomaly detection for cyber-physical system networks. In: Proceedings on cyber-physical systems security and privacy, pp 1–12
Poornima IGA, Paramasivan B (2020) Anomaly detection in wireless sensor network using machine learning algorithm. Comput Commun 151:331–337
Ullah I, Mahmoud QH (2020) A technique for generating a botnet dataset for anomalous activity detection in IoT networks. In: 2020 IEEE transactions on Systems, Man, and Cybernetics (SMC). IEEE, pp 134–140
Panthi M (2020) Anomaly detection in smart grids using machine learning techniques. In: 2020 First International Conference on Power, Control and Computing Technologies (ICPC2T). IEEE
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Swathi, K., Narsimha, G. (2024). Cyber-Attacks and Anomaly Detection in Networking Based on Deep Learning—A Survey. In: Gunjan, V.K., Kumar, A., Zurada, J.M., Singh, S.N. (eds) Computational Intelligence in Machine Learning. ICCIML 2022. Lecture Notes in Electrical Engineering, vol 1106. Springer, Singapore. https://doi.org/10.1007/978-981-99-7954-7_2
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DOI: https://doi.org/10.1007/978-981-99-7954-7_2
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