A Survey of Intrusion Detection Systems Based On Deep Learning for IoT Data
Subject Areas : Machine learning
Mehrnaz Moudi
1
,
Arefeh Soleimani
2
,
AmirHossein Hojjati nia
3
1 - Department of Computer Engineering, University of Torbat Heydarieh
2 - Department of Computer Engineering, University of Torbat Heydarieh
3 - Department of Computer Engineering, University of Torbat Heydarieh
Keywords: Internet of Things, Artificial Intelligence, Machine learning, Deep learning, Intrusion Detection Systems,
Abstract :
Today, the scope of using the Internet of Things is growing by taking science and technology as the first place in human life, and as these networks get bigger, more data are exchanged. It performs high-speed data exchanges on the Internet and in a pre-defined network. The more the Internet of Things penetrates into people's lives, the more important data it transmits. This causes attackers to draw attention to these data, and Internet of Things network devices that have limited resources are exposed to attacks. With the complexity of hardware and software for the ease of human's use, naturally more intelligent attacks will happen, which is the reason of presenting many methods in this field. For this reason, in this article, we are going to discuss the most important methods used in intrusion detection systems based on deep learning and machine that can identify these interruptions. In this article, we have compared 46 articles from 2020 to 2024 based on the type of dataset used, the type of classification (binary or multi-class) and the accuracy rates obtained from each method, and we have been able to see a comprehensive overview for researchers who intend to work in IoT data security. According to the obtained results, if the proposed method is implemented in binary form, it can achieve better accuracy than multi-class.
[1] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, and W. Zhao, "A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications", IEEE internet of things journal, Vol. 4, No. 5, 2017, pp. 1125-1142.
[2] U. Farooq, N. Tariq, M. Asim, T. Baker, and A. Al-Shamma'a, "Machine learning and the Internet of Things security: Solutions and open challenges", Journal of Parallel and Distributed Computing, Vol. 162, 2022, pp. 89-104.
[3] A. Adnan, A. Muhammed, A. A. Abd Ghani, A. Abdullah, and F. Hakim, "An intrusion detection system for the internet of things based on machine learning: Review and challenges", Symmetry, Vol. 13, No. 6, 2021, pp. 1011.
[4] K. Lakshmanna et al., "A review on deep learning techniques for IoT data", Electronics, Vol. 11, No. 10, pp. 1604, 2022.
[5] M. A. Alsoufi, S. Razak, M. M. Siraj, I. Nafea, F. A. Ghaleb, F. Saeed, and M. Nasser, "Anomaly-based intrusion detection systems in iot using deep learning: A systematic literature review", Applied sciences, Vol. 11, No. 18, 2021, pp. 8383.
[6] T. Hossain, M. Ariful Islam, A. B. R. Khan, and M. Sadekur Rahman, "A Robust and Accurate IoT-Based Fire Alarm System for Residential Use", in International Conference of Computer Networks, Big Data and IoT (ICCBI), 2021, Singapore.
[7] B. Lahasan and H. Samma, "Optimized deep autoencoder model for internet of things intruder detection", IEEE Access, Vol. 10, 2022, pp. 8434-8448.
[8] S. Ullah et al., "A new intrusion detection system for the internet of things via deep convolutional neural network and feature engineering", Sensors, Vol. 22, No. 10, 2022, pp. 3607.
[9] N. Tariq, M. Asim, Z. Maamar, M. Z. Farooqi, N. Faci, and T. Baker, "A mobile code-driven trust mechanism for detecting internal attacks in sensor node-powered IoT", Journal of Parallel and Distributed Computing, Vol. 134, 2019, pp. 198-206.
[10] P. Prasdika and B. Sugiantoro, "A review paper on big data and data mining concepts and techniques", International Journal on Informatics for Development, Vol. 7, No. 1, 2018, pp. 36-38.
[11] A. Patcha and J.-M. Park, "An overview of anomaly detection techniques: Existing solutions and latest technological trends", Computer networks, Vol. 51, No. 12, 2007, pp. 3448-3470.
[12] D. Minh, H. X. Wang, Y. F. Li, and T. N. Nguyen, "Explainable artificial intelligence: a comprehensive review", Artificial Intelligence Review, Vol. 55, 2022, pp. 3503-3568.
[13] G. Kumar, K. Kumar, and M. Sachdeva, "The use of artificial intelligence based techniques for intrusion detection: a review", Artificial Intelligence Review, Vol. 34, 2010, pp. 369-387.
[14] C. Janiesch, P. Zschech, and K. Heinrich, "Machine learning and deep learning", Electronic Markets, Vol. 31, No. 3, 2021, pp. 685-695.
[15] A. Paleyes, R.-G. Urma, and N. D. Lawrence, "Challenges in deploying machine learning: a survey of case studies", ACM Computing Surveys, Vol. 55, No. 6, 2022, pp. 1-29.
[16] S. Dong, P. Wang, and K. Abbas, "A survey on deep learning and its applications", Computer Science Review, Vol. 40, 2021, pp. 100379.
[17] S. B. Saad, A. Ksentini, and B. Brik, "A Trust architecture for the SLA management in 5G networks", in IEEE-International Conference on Communications (ICC), 2021, Canada, pp. 1-6.
[18] A. Thakkar and R. Lohiya, "Role of swarm and evolutionary algorithms for intrusion detection system: A survey", Swarm and evolutionary computation, Vol. 53, 2020, pp. 100631.
[19] A. Thakkar and R. Lohiya, "A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges", Archives of Computational Methods in Engineering, Vol. 28, 2021, pp. 3211-3243.
[20] Y. Yue, S. Li, P. Legg, and F. Li, "Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey", Security and communication Networks, Vol. 2021, 2021, pp. 1-13.
[21] J. Porras, J. Khakurel, A. Knutas, and J. Pänkäläinen, "Security challenges and solutions in the internet of things", Nordic and Baltic Journal of Information and Communications Technologies, Vol. 2018, No. 1, 2018, pp. 177-206.
[22] J. Wurm, K. Hoang, O. Arias, A.-R. Sadeghi, and Y. Jin, "Security analysis on consumer and industrial IoT devices", in 21st Asia and South Pacific design automation conference (ASP-DAC), 2016, China, pp. 519-524.
[23] S. Bharati and P. Podder, "Machine and deep learning for iot security and privacy: applications, challenges, and future directions", Security and Communication Networks, Vol. 2022, 2022, pp. 1-41.
[24] M. Mohammadi, A. Al-Fuqaha, S. Sorour, and M. Guizani, "Deep learning for IoT big data and streaming analytics: A survey", IEEE Communications Surveys & Tutorials, Vol. 20, No. 4, 2018, pp. 2923-2960.
[25] J. Franklin, "The elements of statistical learning: data mining, inference and prediction", The Mathematical Intelligencer, Vol. 27, No. 2, 2005, pp. 83-85.
[26] G. E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks", science, Vol. 313, No. 5786, 2006, pp. 504-507.
[27] Z. M. Fadlullah, F. Tang, B. Mao, N. Kato, O. Akashi, T. Inoue, and K. Mizutani, "State-of-the-art deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems", IEEE Communications Surveys & Tutorials, Vol. 19, No. 4, 2017, pp. 2432-2455.
[28] H. Li, K. Ota, and M. Dong, "Learning IoT in edge: Deep learning for the Internet of Things with edge computing", IEEE network, Vol. 32, No. 1, 2018, pp. 96-101.
[29] H. Kim, S. Park, H. Hong, J. Park, and S. Kim, "A Transferable Deep Learning Framework for Improving the Accuracy of Internet of Things Intrusion Detection", Future Internet, Vol. 16, No. 3, 2024, pp. 80.
[30] E. Osa, P. E. Orukpe, and U. Iruansi, "Design and implementation of a deep neural network approach for intrusion detection systems", e-Prime-Advances in Electrical Engineering, Electronics and Energy, Vol. 7, 2024, pp. 100434.
[31] K. Psychogyios, A. Papadakis, S. Bourou, N. Nikolaou, A. Maniatis, and T. Zahariadis, "Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data", Future Internet, Vol. 16, No. 3, 2024, pp. 73.
[32] Ü. Çavuşoğlu, D. Akgun, and S. Hizal, "A novel cyber security model using deep transfer learning", Arabian Journal for Science and Engineering, Vol. 49, No. 3, 2024, pp. 3623-3632.
[33] Y. Yang, J. Cheng, Z. Liu, H. Li, and G. Xu, "A multi-classification detection model for imbalanced data in NIDS based on reconstruction and feature matching", Journal of Cloud Computing, Vol. 13, No. 1, 2024, pp. 31.
[34] V. Hnamte, H. Nhung-Nguyen, J. Hussain, and Y. Hwa-Kim, "A novel two-stage deep learning model for network intrusion detection: LSTM-AE", IEEE Access, Vol. 11, 2023, pp. 37131-37148.
[35] N. Alenezi and A. Aljuhani, "Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques", Computer Systems Science & Engineering, Vol. 46, No. 3, 2023, pp. 2899-2915.
[36] U. K. Lilhore et al., "HIDM: Hybrid intrusion detection model for industry 4.0 Networks using an optimized CNN-LSTM with transfer learning", Sensors, Vol. 23, No. 18, 2023, pp. 7856.
[37] J. Figueiredo, C. Serrão, and A. M. de Almeida, "Deep learning model transposition for network intrusion detection systems", Electronics, Vol. 12, No. 2, 2023, pp. 293.
[38] R. Chaganti, W. Suliman, V. Ravi, and A. Dua, "Deep learning approach for SDN-enabled intrusion detection system in IoT networks", Information, Vol. 14, No. 1, 2023, pp. 41.
[39] S. K. Gupta, M. Tripathi, and J. Grover, "Hybrid optimization and deep learning based intrusion detection system", Computers and Electrical Engineering, Vol. 100, 2022, pp. 107876.
[40] A. Basati and M. M. Faghih, "DFE: Efficient IoT network intrusion detection using deep feature extraction", Neural Computing and Applications, Vol. 34, No. 18, 2022, pp. 15175-15195.
[41] A. Sagu, N. S. Gill, P. Gulia, J. M. Chatterjee, and I. Priyadarshini, "A hybrid deep learning model with self-improved optimization algorithm for detection of security attacks in IoT environment", Future Internet, Vol. 14, No. 10, 2022, pp. 301.
[42] I. Idrissi, M. Mostafa Azizi, and O. Moussaoui, "A lightweight optimized deep learning-based host-intrusion detection system deployed on the edge for IoT", International Journal of Computing and Digital System, Vol. 11, No. 1, 2021, pp. 209-216.
[43] Y. Masoudi-Sobhanzadeh and S. Emami-Moghaddam, "A real-time IoT-based botnet detection method using a novel two-step feature selection technique and the support vector machine classifier", Computer Networks, Vol. 217, 2022, pp. 109365.
[44] K. Malik, F. Rehman, T. Maqsood, S. Mustafa, O. Khalid, and A. Akhunzada, "Lightweight internet of things botnet detection using one-class classification", Sensors, Vol. 22, No. 10, 2022, pp. 3646.
[45] S. Diddi, S. Lohidasan, S. Arulmozhi, and K. R. Mahadik, "Standardization and Ameliorative effect of Kalyanaka ghrita in β-amyloid induced memory impairment in wistar rats", Journal of Ethnopharmacology, Vol. 300, 2023, pp. 115671.
[46] I. Idrissi, M. Boukabous, M. Azizi, O. Moussaoui, and H. El Fadili, "Toward a deep learning-based intrusion detection system for IoT against botnet attacks", IAES International Journal of Artificial Intelligence, Vol. 10, No. 1, 2021, pp. 110.
[47] H. Alkahtani and T. H. Aldhyani, "Intrusion detection system to advance internet of things infrastructure-based deep learning algorithms", Complexity, Vol. 2021, 2021, pp. 1-18.
[48] L. Liu, P. Wang, J. Lin, and L. Liu, "Intrusion detection of imbalanced network traffic based on machine learning and deep learning", IEEE Access, Vol. 9, 2020, pp. 7550-7563.
[49] J. Ashraf, A. D. Bakhshi, N. Moustafa, H. Khurshid, A. Javed, and A. Beheshti, "Novel deep learning-enabled LSTM autoencoder architecture for discovering anomalous events from intelligent transportation systems", IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 7, 2020, pp. 4507-4518.
[50] B. Borisenko, S. Erokhin, A. Fadeev, and I. Martishin, "Intrusion detection using multilayer perceptron and neural networks with long short-term memory", in Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), 2021, Russia, pp. 1-6.
[51] T. H. Hai and L. H. Nam, "A practical comparison of deep learning methods for network intrusion detection", in International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 2021, Malaysia, pp. 1-6.
[52] T. Pooja and P. Shrinivasacharya, "Evaluating neural networks using Bi-Directional LSTM for network IDS (intrusion detection systems) in cyber security", Global Transitions Proceedings, Vol. 2, No. 2, 2021, pp. 448-454.
[53] S. N. Mighan and M. Kahani, "A novel scalable intrusion detection system based on deep learning", International Journal of Information Security, Vol. 20, No. 3, 2021, pp. 387-403.
[54] H. Jia, J. Liu, M. Zhang, X. He, and W. Sun, "Network intrusion detection based on IE-DBN model", Computer Communications, Vol. 178, 2021, pp. 131-140.
[55] R. Biswas and S. Roy, "Botnet traffic identification using neural networks", Multimedia Tools and Applications, Vol. 80, 2021, pp. 24147-24171.
[56] F. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, "Intrusion detection systems using long short-term memory (LSTM)", Journal of Big Data, Vol. 8, No. 1, 2021, pp. 65.
[57] M. S. ElSayed, N.-A. Le-Khac, M. A. Albahar, and 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, Vol. 191, 2021, pp. 103160.
[58] C. Joshi, R. K. Ranjan, and V. Bharti, "A Fuzzy Logic based feature engineering approach for Botnet detection using ANN", Journal of King Saud University-Computer and Information Sciences, Vol. 34, No. 9, 2022, pp. 6872-6882.
[59] K. Sethi, Y. V. Madhav, R. Kumar, and P. Bera, "Attention based multi-agent intrusion detection systems using reinforcement learning", Journal of Information Security and Applications, Vol. 61, 2021, pp. 102923.
[60] R. V. Mendonca, J. C. Silva, R. L. Rosa, M. Saadi, D. Z. Rodriguez, and A. Farouk, "A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms", Expert Systems, Vol. 39, No. 5, 2022, pp. e12917.
[61] F. Hussain et al., "A framework for malicious traffic detection in IoT healthcare environment", Sensors, Vol. 21, No. 9, 2021, pp. 3025.
[62] I. Vaccari, S. Narteni, M. Aiello, M. Mongelli, and E. Cambiaso, "Exploiting Internet of Things protocols for malicious data exfiltration activities", IEEE Access, Vol. 9, 2021, pp. 104261-104280.
[63] Y. Imrana, Y. Xiang, L. Ali, and Z. Abdul-Rauf, "A bidirectional LSTM deep learning approach for intrusion detection", Expert Systems with Applications, Vol. 185, 2021, pp. 115524.
[64] I. A. Khan, N. Moustafa, D. Pi, W. Haider, B. Li, and A. Jolfaei, "An enhanced multi-stage deep learning framework for detecting malicious activities from autonomous vehicles", IEEE Transactions on Intelligent Transportation Systems, Vol. 23, No. 12, 2021, pp. 25469-25478.
[65] G. D. L. T. Parra, P. Rad, K.-K. R. Choo, and N. Beebe, "Detecting Internet of Things attacks using distributed deep learning", Journal of Network and Computer Applications, Vol. 163, 2020, pp. 102662.
[66] S. Latif, Z. Idrees, Z. Zou, and J. Ahmad, "DRaNN: A deep random neural network model for intrusion detection in industrial IoT", in International Conference On UK-China Emerging Technologies (UCET), 2020, Glasgow, UK, pp. 1-4.
[67] M. Roopak, G. Y. Tian, and J. Chambers, "An intrusion detection system against ddos attacks in iot networks", in 10th annual computing and communication workshop and conference (CCWC), 2020, USA, pp. 0562-0567.
[68] S. Smys, A. Basar, and H. Wang, "Hybrid intrusion detection system for internet of things (IoT)", Journal of ISMAC, Vol. 2, No. 04, 2020, pp. 190-199.
[69] S. M. Kasongo and Y. Sun, "Performance analysis of intrusion detection systems using a feature selection method on the UNSW-NB15 dataset", Journal of Big Data, Vol. 7, 2020, pp. 1-20.
[70] Y. Li et al., "Robust detection for network intrusion of industrial IoT based on multi-CNN fusion", Measurement, Vol. 154, 2020, pp. 107450.
[71] R. Abou Khamis and A. Matrawy, "Evaluation of adversarial training on different types of neural networks in deep learning-based idss", in International Symposium On Networks, Computers And Communications (ISNCC), 2020, Canada, IEEE, pp. 1-6.