Deep learning-based intrusion detection systems: A comprehensive survey of four main fields of cyber security

[1] J. Jang-Jaccard, S. Nepal, A Survey of Emerging Threats in Cybersecurity, Journal of Computer and System Sciences, 80, 5, (2014), 973-993.

[2] K. S. Vanitha, S. V. UMA, S. K. Mahidhar, Distributed Denial of Service: Attack Techniques and Mitigation, 2017 International Conference on Circuits, Controls, and Communications (CCUBE), (2017), 226-231.

[3] P. G. Govind, M. Kulariya, A Framework for Fast and Ecient Cyber Security Network Intrusion Detection Using Apache Spark, Procedia Computer Science, 93, (2016), 824-831.

[4] S. Jin, J. -G. Chung, Y. Xu, Signature-Based Intrusion Detection System (IDS) for In-Vehicle CAN Bus Network, 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021, 1-5.

[5] N. T. Van, T. N. Thinh, L. T. Sach, An Anomaly-Based Network Intrusion Detection System Using Deep learning, 2017 International Conference on System Science and Engineering (ICSSE), (2017), 210-214.

[6] M. Hoque, M. Mukit, A. Bikas, An Implementation of Intrusion Detection System Using Genetic Algorithm, International Journal of Network Security & Its Applications, 4, 2, (2012), 109-120.

[7] S. Mohammadi, H. Mirvaziri, M. G. Ahsaee, H. Karimipour, Cyber Intrusion Detection by Combined Feature Selection Algorithm, Journal of Information Security and Applications, 44, (2019), 80-88.

[8] M. Usama et al., Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges, IEEE Access, 7, (2019), 65579-65615.

[9] H. Liao, C. Lin, Y. Lin, K. Tung, Intrusion Detection System: A Comprehensive Review, Journal of Network and Computer Applications, 36, 1 (2013), 16-24.

[10] A. Khraisat, I. Gondal, P. Vamplew, J. Kamruzzaman, Survey of Intrusion Detection Systems: Techniques, Datasets and Challenges, Cybersecure 2, 20 (2019).

[11] M. A. Ferrag, L. Maglaras, S. Moschoyiannis, H. Janicke, Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study, Journal of Information Security and Applications, 50, (2020), 102419.

[12] A. Aldweesh, A. Derhab, A. Z. Emam, Deep Learning Approaches for Anomaly-Based Intrusion Detection Systems: A Survey, Taxonomy, and Open Issues, Knowledge-Based Systems, 189, (2020), 105124.

[13] A. M. Aleesa, B. B. Zaidan, A. A. Zaidan, N. M. Sahar, Review of Intrusion Detection Systems Based on Deep Learning Techniques: Coherent Taxonomy, Challenges, Motivations, Recommendations, Substantial Analysis and Future Directions. Neural Comput & Applic 32, (2020), 9827{9858.

[14] G.E. Hinton, Deep Belief Networks, Scholarpedia, 4, (2009), 5947.

[15] N. M. Rezk, M. Purnaprajna, T. Nordstrom, Z. Ul-Abdin, Recurrent Neural Networks: An Embedded Computing Perspective, IEEE Access, 8, (2020), 57967-57996.

[16] L. Gonog and Y. Zhou, A Review: Generative Adversarial Networks, 2019 14th IEEE Conference on Industrial Electronics and Applications (ICIEA), (2019), 505-510.

[17] R. Vinayakumar, K. P. Soman, P. Poornachandran, Applying Convolutional Neural Network for Network Intrusion Detection, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), (2017), 1222-1228.

[18] S. Hochreiter, J. Schmidhuber, Long Short-Term Memory, Neural Computation, 9, 8, (1997), 1735-1780.

[19] H. Ma, Pattern Recognition Using Boltzmann Machine, Proceedings IEEE Southeastcon '95. Visualize the Future, (1995), 23-29.

[20] O. Kaynar, A. G. Yuksek, Y. Gormez and Y. E. Isik, Intrusion Detection with Autoencoder Based Deep Learning Machine, 2017 25th Signal Processing and Communications Applications Conference (SIU), (2017), 1-4.

[21] https://www.unb.ca/cic/datasets/nsl.html

[22] https://kdd.ics.uci.edu/databases/kddcup99/task.html

[23] https://www.unb.ca/cic/datasets/ddos-2019.html#: :text=2.,%2Dworld%20data%20(PCAPs).

[24] N. Moustafa, J. Slay, UNSW-NB15: A Comprehensive Data Set for Network Intrusion Detection Systems (UNSW-NB15 network data set), 2015 Military Communications and Information Systems Conference (MilCIS), (2015), 1-6.

[25] https://www.unb.ca/cic/datasets/ids-2017.html

[26] https://www.stratosphereips.org/datasets-iot23#:text=IoT%2D23%20is%20a%20new,of%20Things%20(IoT)%20devices.&text=Its%20goal%20is%20to%20o er,funded%20by%20Avast%20Software%2C%20Prague.

[27] https://archive.ics.uci.edu/ml/datasets/detection of IoT botnet attacks N Bait

[28] https://ieee-dataport.org/documents/bot-iot-dataset

[29] https://ieeexplore.ieee.org/document/9187858

[30] https://www.unb.ca/cic/datasets/ddos-2019.html#: :text=2.,%2Dworld%20data%20(PCAPs)

[31] https://www.semanticscholar.org/paper/Industrial-Control-System-Simulation-and-Data-for-Morris-Thornton/bb9714e0c661576f5df19fb54e0e26567ca37372

[32] S. M. Kasongo, Y. Sun, A Deep Gated Recurrent Unit Based Model for Wireless Intrusion Detection System, ICT Express, 7, 1, (2021), 81-87.

[33] B. Riyaz, S. Ganapathy, A Deep Learning Approach for E ective Intrusion Detection in Wireless Networks Using CNN. Soft Comput, 24, (2020), 17265{17278.

[34] S. M. Kasongo, Y. Sun, A Deep Long Short-Term Memory Based Classi er for Wireless Intrusion Detection System, ICT Express, 6, 2, (2020), 98-103.

[35] V. Gowdhaman, R. Dhanapal, An Intrusion Detection System for Wireless Sensor Networks Using Deep Neural Network, Soft Comput (2021).

[36] Q. Duan, X. Wei, J. Fan, L. Yu, Y. Hu, CNN-based Intrusion Classi cation for IEEE 802.11 Wireless Networks, 2020 IEEE 6th International Conference on Computer and Communications (ICCC), (2020), 830-833.

[37] O. Sbai, M. El-boukhari, Data Flooding Intrusion Detection System for MANETs Using Deep Learning Approach, Proceedings of the 13th International Conference on Intelligent Systems: Theories and Applications (SITA'20), 46, (2020), 1{5.

[38] S. Dilipkumar, M. Durairaj, Epilson Swarm Optimized Cluster Gradient and Deep Belief Classi er for Multi-Attack Intrusion Detection in MANET, J Ambient Intell Human Comput, (2021).

[39] S. Huang, K. Lei, IGAN-IDS: An Imbalanced Generative Adversarial Network Towards Intrusion Detection System in Ad-Hoc Networks, Ad Hoc Networks, 105, (2020), 102177.

[40] M. D. Hossain, H. Inoue, H. Ochiai, D. Fall, Y. Kadobayashi, LSTM-Based Intrusion Detection System for In-Vehicle Can Bus Communications, in IEEE Access, 8, (2020), 185489-185502.

[41] H. Yang, F. Wang, Wireless Network Intrusion Detection Based on Improved Convolutional Neural Network, IEEE Access, 7, (2019), 64366-64374.

[42] D. Neema, G. Raina, K. P. Jagannathan, A Framework for End-to-End Deep Learning-Based Anomaly Detection in Transportation Networks, Transportation Research Interdisciplinary Perspectives, 5, (2020), 100112.

[43] S. M. Kasongo, Y. Sun, A Deep Learning Method with Wrapper Based Feature Extraction for Wireless Intrusion Detection System, Computers & Security, 92, (2020), 101752.

[44] J. Zhang, F. Li, H. Zhang, R. Li, Y. Li, Intrusion Detection System Using Deep Learning for In-Vehicle Security, Ad Hoc Networks, 95, (2019), 101974.

[45] M. Aloqaily, S. Otoum, I. A. Ridhawi, Y. Jararweh, An Intrusion Detection System for Connected Vehicles in Smart Cities, Ad Hoc Networks, 90, (2019), 101842.

[46] M. P. Novaes, L. F. Carvalho, J. Lloret, M. L. Proenca, Adversarial Deep Learning Approach Detection and Defense Against DDoS Attacks in SDN Environments, Future Generation Computer Systems, 125, (2021), 156-167.

[47] M. Abdallah, N. A. L. Khac, H. Jahromi, A. D. Jurcut, A Hybrid CNN-LSTM Based Approach for Anomaly Detection Systems in SDNs, The 16th International Conference on Availability, Reliability and Security (ARES 2021), 34, (2021), 1-7.

[48] M. S. ElSayed, N. Le-Khac, M. A. Albahar, A. Jurcut, A Novel Hybrid Model for Intrusion Detection Systems in SDNs Based on CNN and a New Regularization Technique, Journal of Network and Computer Applications, 191, (2021), 103160.

[49] T. A. Tang. L. Mhamdi, D. McLernon, S. A. R. Zaidi, M. Ghogho, F. El Moussa, DeepIDS: Deep Learning Approach for Intrusion Detection in Software De ned Net-working. Electronics, 9, (2020), 1533.

[50] T. -H. Lee, L. -H. Chang, C. -W. Syu, Deep Learning Enabled Intrusion Detection and Prevention System over SDN Networks, 2020 IEEE International Conference on Communications Workshops (ICC Workshops), (2020), 1-6.

[51] A. Makuvaza, D. S. Jat, A. M. Gamundani, Deep Neural Network (DNN) Solution for Real-time Detection of Distributed Denial of Service (DDoS) Attacks in Software De ned Networks (SDNs). SN COMPUT. SCI. 2, (2021), 107.

[52] T. A. Tang, L. Mhamdi, D. McLernon, S. A. R. Zaidi and M. Ghogho, Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks,(2018) 4th IEEE Conference on Network Softwarization and Workshops (NetSoft), (2018).

[53] C. Li, Y. Wu, X. Yuan, et al., Detection and defense of DDoS attack{based on deep learning in OpenFlow-based SDN. Int J Commun Syst. (2018).

[54] J. Malik, A. Akhunzada, I. Bibi, M. Imran, A. Musaddiq, S. W. Kim, Hybrid Deep Learning: An Ecient Reconnaissance and Surveillance Detection Mechanism in SDN, IEEE Access, (2020), 8, 134695-134706.

[55] B. Susilo and R. F. Sari, Intrusion Detection in Software De ned Network Using Deep Learning Approach, 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), (2021), 0807-0812.

[56] S. BOUKRIA and M. GUERROUMI, Intrusion detection system for SDN network using deep learning approach, 2019 International Conference on Theoretical and Applicative Aspects of Computer Science (ICTAACS), (2019).

[57] L. H. Albahar, M. Al, Recurrent Neural Network Model Based on a New Regularization Technique for Real-Time Intrusion Detection in SDN Environments, Security and Communication Network, (2019).

[58] P. Choobdar, M. Naderan, M. Naderan, Detection and Multi-Class Classi cation of Intrusion in Software De ned Networks Using Stacked Auto-Encoders and CICIDS2017 Dataset. Wireless Pers Commun 123, (2022), 437{471.

[59] B. Roy, H. Cheung, A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network, 2018 28th International Telecommunication Networks and Applications Conference (ITNAC), 2018.

[60] V. Dutta, M. Choras, M. Pawlicki, R. Kozik, A Deep Learning Ensemble for Network Anomaly and Cyber-Attack Detection, Sensors, (2020), 20, 16.

[61] X. Kan, Y. Fan, Z. Fang, L. Cao, N. N. Xiong, D. Yang, X. Li, A novel IoT network intrusion detection approach based on Adaptive Particle Swarm Optimization Convolutional Neural Network, Information Sciences, (2021), 568.

[62] A. Telikani, A. H. Gandomi, Cost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things, Internet of Things, (2021), 14.

[63] M. Almiani, A. AbuGhazleh, A. A.-Rahayfeh, S. Atiewi, A. Razaque, Deep recurrent neural network for IoT intrusion detection system, Simulation Modelling Practice and Theory, (2020). 101.

[64] A. Nagisetty , G. P. Gupta, Framework for Detection of Malicious Activities in IoT Networks using Keras Deep Learning Library, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), (2019).

[65] A. Ferdowsi and W. Saad, Generative Adversarial Networks for Distributed Intrusion Detection in the Internet of Things, 2019 IEEE Global Communications Conference (GLOBECOM), (2019).

[66] Y. Zhang, P. Li and X. Wang, Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network, in IEEE Access, (2019), 7, 31711-31722.

[67] A. Elsaeidy, K. S. Munasinghe, D. Sharma, A. Jamalipour, Intrusion detection in smart cities using Restricted Boltzmann Machines, Journal of Network and Computer Applications, (2019), 135.

[68] X.Wang, A. Derhab, A, E. Aldweesh, A. Z. Khan, F. Aslam, Intrusion Detection System for Internet of Things Based on Temporal Convolution Neural Network and Ecient Feature Engineering, (2020).

[69] R. Gassais, N. E.-Jivan, J.M. Fernandez, et al., Multi-level host-based intrusion detection system for Internet of things. J Cloud Comp 9, (2020), 62.

[70] Y. Meidan et al., N-BaIoT|Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders, in IEEE Pervasive Computing, 17, 3, (2018), 12-22.

[71] Y. Li, Y. Xu, Z. Liu, H. Hou, Y. Zheng, Y. Xin, Y. Zhao, L. Cui, Robust detection for network intrusion of industrial IoT based on multi-CNN fusion, Measurement, (2020), 154.

[72] W. Wang, J. Guo, Z. Wang, H. Wang, J. Cheng, C. Wang, M. Yuan, J. Kurths, X. Luo, Y. Gao, Abnormal Flow Detection in Industrial Control Network Based on Deep Reinforcement Learning, Applied Mathematics and Computation, (2021), 409.

[73] S. Huda, S. Miah, J. Yearwood, S. Alyahya, H. Al-Dossari, R. Doss, A Malicious Threat Detection Model for Cloud Assisted Internet of Things (CoT) Based Industrial Control System (ICS) Networks Using Deep Belief Network, Journal of Parallel and Distributed Computing, (2018), 120.

[74] M. Lan, J. Luo, S. Chai, R. Chai, C. Zhang, B. Zhang, A Novel Industrial Intrusion Detection Method based on Threshold-optimized CNN-BiLSTM-Attention using ROC Curve, 2020 39th Chinese Control Conference (CCC), (2020).

[75] J. Liu, L. Yin, Y. Hu, S. Lv, L. Sun, A Novel Intrusion Detection Algorithm for Industrial Control Systems Based on CNN and Process State Transition, 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), (2018).

[76] S. K. Alabugin, A. N. Sokolov, Applying of Generative Adversarial Networks for Anomaly Detection in Industrial Control Systems, 2020 Global Smart Industry Conference (GloSIC), (2020), 199-203

[77] W. Wang, F. Harrou, B. Bouyeddou et al. A Stacked Deep Learning Approach to Cyber-Attacks Detection in Industrial Systems: Application to Power System and Gas Pipeline Systems, Cluster Comput 25, (2022), 561{578.

[78] S. Han, M. Xie, H. -H. Chen, Y. Ling, Intrusion Detection in Cyber-Physical Systems: Techniques and Challenges, IEEE Systems Journal, 8, 4, (2014), 1052-1062.

[79] B. Li, Y. Wu, J. Song, R. Lu, T. Li, L. Zhao, DeepFed: Federated Deep Learning for Intrusion Detection in Industrial Cyber{Physical Systems, IEEE Transactions on Industrial Informatics, 17, 8, (2021), 5615-5624.

[80] H. Yang, L. Cheng, M. C. Chuah, Deep-Learning-Based Network Intrusion Detection for SCADA Systems, 2019 IEEE Conference on Communications and Network Security (CNS), (2019).

[81] A.A. Suzen, Developing a Multi-level Intrusion Detection System Using Hybrid-DBN. J Ambient Intell Human Comput 12, (2021), 1913{1923.

[82] C. Galdi, A. Chu, Y. Lai, J. Liu, Industrial Control Intrusion Detection Approach Based on Multiclassi cation GoogLeNet-LSTM Model, (2019).

[83] F. Xingjie, W. Guogenp, Z. ShiBIN, ChenHAO, Industrial Control System Intrusion Detection Model based on LSTM & Attack Tree, 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), (2020).

[84] Y. Li, Y. Li, S. Zhang. Intrusion Detection Algorithm Based on Deep Learning for Industrial Control Networks. In Proceedings of the 2019 The 2nd International Conference on Robotics, Control and Automation Engineering (RCAE 2019). (2019), 40{44.

[85] G. B. Huang, Q. Y. Zhu, C. K. Siew, Extreme Learning Machine: Theory and Applications, Neu-rocomputing, 70(1-3), (2006), 489-501.

[86] C.-H. Chen, C. Wang, B. Wang, Y. Sun, Y. Wei, K. Z. Wang, L. H. Hui, Intrusion Detection for Industrial Control Systems Based on Open Set Arti cial Neural Network, (2021).

[87] A. N. Sokolov, S. K. Alabugin, I. A. Pyatnitsky, Trac Modeling by Recurrent Neural Networks for Intrusion Detection in Industrial Control Systems, 2019 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM), (2019).

[88] J. Shu, L. Zhou, W. Zhang, X. Du, M. Guizani, Collaborative Intrusion Detection for VANETs: A Deep Learning-Based Distributed SDN Approach, in IEEE Transactions on Intelligent Transportation Systems, 22, 7, (2021), 4519-4530.

[89] D. Li, L. Deng, M. Lee, H. Wang, IoT Data Feature Extraction and Intrusion Detection System for Smart Cities Based on Deep Migration Learning, International Journal of Information Management, (2019), 49

[90] H. Polat, M. Turkoglu, O. Polat, Deep Network Approach with Stacked Sparse Autoen-coders in Detection of DDoS Attacks on SDN-based VANET, IET Communications, 14, (2020), 4089-4100.

[91] D. Javeed, T. Gao, M. T. Khan, SDN-Enabled Hybrid DL-Driven Framework for the Detection of Emerging Cyber Threats in IoT, 10, 8, (2021), 918.

[92] M. Arif, I. Ullah, B. A. Raza, A. Sikandar, A. Irshad, S. Baseer, A. Irshad, Software De ned Network Enabled Fog-to-Things Hybrid Deep Learning Driven Cyber Threat Detection System, (2021), 6136670.

[93] M. Al-Hawawreh, E. Sitnikova, F. Hartog, An Ecient Intrusion Detection Model for Edge System in Brown eld Industrial Internet of Things, Proceedings of the 3rd International Conference on Big Data and Internet of Things (BDIOT 2019). (2019).

[94] L. Nie, Z. Ning, X. Wang, X. Hu, J. Cheng, Y. Li, Data-Driven Intrusion Detection for Intelligent Internet of Vehicles: A Deep Convolutional Neural Network-Based Method, IEEE Transactions on Network Science and Engineering, 7, 4, (2020), 2219-2230.

[95] S. Latif, Z. Idrees, Z. Zou, J. Ahmad, DRaNN: A Deep Random Neural Network Model for Intrusion Detection in Industrial IoT, 2020 International Conference on UK-China Emerging Technologies (UCET), (2020).

[96] M. Al-Hawawreh, E. Sitnikova. Industrial Internet of Things Based Ransomware Detection using Stacked Variational Neural Network, In Proceedings of the 3rd International Conference on Big Data and Internet of Things (BDIOT 2019), (2019), 126{130.