Comparative Study on Anomaly based Intrusion Detection using Deep Learning Techniques
DOI:
https://doi.org/10.4108/eetiot.7178Keywords:
Wireless Sensor Network, Malicious Attacks, Intrusion Detection Systems, Machine Learning, Deep LearningAbstract
With an array of applications, Wireless Sensor Networks (WSNs) have the potential to transform the world into a smart planet. WSNs consist of a collection of resource-constrained sensors that gather data, which is then utilized for decision-making and analysis, leading to improvements in quality of service, management, and efficiency. However, the open nature of WSNs exposes them to numerous vulnerabilities and threats. Operating in potentially hostile and unattended environments makes these networks attractive targets for adversaries. Therefore, it is essential to detect the presence of malicious attacks within the networks and implement robust security systems to address these challenges. While traditional security mechanisms such as authentication and cryptographic methods are commonly employed, they often fall short in effectively countering the dynamic nature of modern attacks. Hence, IDS (Intrusion Detection System) tends to continuously monitor the network and detect potential threats in real-time scenarios. This method possess the ability of identifying, responding promptly, preventing and thus ensures resilience of the network. Therefore, the present study reviews the various intrusion detection techniques and data collection methods. The main aim of the study is to investigate the design challenges of deploying IDS in a WSN environment. So, the study analysed the AI (Artificial Intelligence) based techniques involved in intrusion detection and how these techniques could be adopted in WSN. In addition, the comparative analysis of several ML (Machine Learning) and DL (Deep Learning) algorithms are also deliberated to portray the different deployment technique with corresponding outcomes. Further, the main challenges faced by each studies with their limitations are specified for supporting future researchers in developing new trends in intrusion detection for WSN.
Downloads
References
[1] D. Kandris, C. Nakas, D. Vomvas, and G. Koulouras, "Applications of wireless sensor networks: an up-to-date survey," Applied system innovation, vol. 3, no. 1, p. 14, 2020.
[2] R. Sarath Kumar, P. Sampath, and M. Ramkumar, "Enhanced Elman Spike Neural Network fostered intrusion detection framework for securing wireless sensor network," Peer-to-Peer Networking and Applications, pp. 1-15, 2023.
[3] C. Nakas, D. Kandris, and G. Visvardis, "Energy efficient routing in wireless sensor networks: A comprehensive survey," Algorithms, vol. 13, no. 3, p. 72, 2020.
[4] A. Khraisat, I. Gondal, P. Vamplew, and J. Kamruzzaman, "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, pp. 1-22, 2019.
[5] R. Zhang and X. Xiao, "Intrusion detection in wireless sensor networks with an improved NSA based on space division," Journal of Sensors, vol. 2019, 2019.
[6] R.-H. Dong, H.-H. Yan, and Q.-Y. Zhang, "An Intrusion Detection Model for Wireless Sensor Network Based on Information Gain Ratio and Bagging Algorithm," Int. J. Netw. Secur., vol. 22, no. 2, pp. 218-230, 2020.
[7] S. Jiang, J. Zhao, and X. Xu, "SLGBM: An intrusion detection mechanism for wireless sensor networks in smart environments," IEEE Access, vol. 8, pp. 169548-169558, 2020.
[8] M. Adil, M. A. Almaiah, A. Omar Alsayed, and O. Almomani, "An anonymous channel categorization scheme of edge nodes to detect jamming attacks in wireless sensor networks," Sensors, vol. 20, no. 8, p. 2311, 2020.
[9] O. I. Khalaf and B. M. Sabbar, "An overview on wireless sensor networks and finding optimal location of nodes," Periodicals of Engineering and Natural Sciences, vol. 7, no. 3, pp. 1096-1101, 2019.
[10] A. Sarkar and T. Senthil Murugan, "Cluster head selection for energy efficient and delay-less routing in wireless sensor network," Wireless Networks, vol. 25, pp. 303-320, 2019.
[11] A. S. Toor and A. Jain, "Energy aware cluster based multi-hop energy efficient routing protocol using multiple mobile nodes (MEACBM) in wireless sensor networks," AEU-International Journal of Electronics and Communications, vol. 102, pp. 41-53, 2019.
[12] B. Bhushan and G. Sahoo, "Requirements, protocols, and security challenges in wireless sensor networks: An industrial perspective," Handbook of computer networks and cyber security: principles and paradigms, pp. 683-713, 2020.
[13] D. E. Boubiche, S. Athmani, S. Boubiche, and H. Toral-Cruz, "Cybersecurity issues in wireless sensor networks: current challenges and solutions," Wireless Personal Communications, vol. 117, pp. 177-213, 2021.
[14] H. A. Babaeer and S. A. Al-Ahmadi, "Efficient and secure data transmission and sinkhole detection in a multi-clustering wireless sensor network based on homomorphic encryption and watermarking," IEEE Access, vol. 8, pp. 92098-92109, 2020.
[15] Z. Chiba, N. Abghour, K. Moussaid, A. El Omri, and M. Rida, "New anomaly network intrusion detection system in cloud environment based on optimized back propagation neural network using improved genetic algorithm," International Journal of Communication Networks and Information Security, vol. 11, no. 1, pp. 61-84, 2019.
[16] S. Venkatraman and B. Surendiran, "Adaptive hybrid intrusion detection system for crowd sourced multimedia internet of things systems," Multimedia Tools and Applications, vol. 79, no. 5-6, pp. 3993-4010, 2020.
[17] H. Bai, X. Zhang, and F. Liu, "Intrusion detection algorithm based on change rates of multiple attributes for WSN," Wireless Communications and Mobile Computing, vol. 2020, pp. 1-16, 2020.
[18] T. Ghazal, "Data Fusion-based machine learning architecture for intrusion detection," Computers, Materials & Continua, vol. 70, no. 2, pp. 3399-3413, 2022.
[19] A. F. J. Jasim and S. Kurnaz, "New automatic (IDS) in IoTs with artificial intelligence technique," Optik, vol. 273, p. 170417, 2023.
[20] A. B. Abhale, "Deep Learning Perspectives to Detecting Intrusions in Wireless Sensor Networks," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2s, pp. 18-26, 2023.
[21] M. Aljebreen et al., "Binary Chimp Optimization Algorithm with ML Based Intrusion Detection for Secure IoT-Assisted Wireless Sensor Networks," Sensors, vol. 23, no. 8, p. 4073, 2023.
[22] S. Subbiah, K. S. M. Anbananthen, S. Thangaraj, S. Kannan, and D. Chelliah, "Intrusion detection technique in wireless sensor network using grid search random forest with Boruta feature selection algorithm," Journal of Communications and Networks, vol. 24, no. 2, pp. 264-273, 2022.
[23] H. Tabbaa, S. Ifzarne, and I. Hafidi, "An online ensemble learning model for detecting attacks in wireless sensor networks," arXiv preprint arXiv:2204.13814, 2022.
[24] P. KANAGAVALLI and D. KARTHIKEYANI, "INVESTIGATION OF INTRUSION DETECTION SYSTEM USING RANDOM FOREST, CART AND PROPOSED SECURE RANDOM FOREST ALGORITHMS (SRFA)," Journal of Theoretical and Applied Information Technology, vol. 101, no. 3, 2023.
[25] X. Tan et al., "Wireless sensor networks intrusion detection based on SMOTE and the random forest algorithm," Sensors, vol. 19, no. 1, p. 203, 2019.
[26] S. D. D. Anton, S. Sinha, and H. D. Schotten, "Anomaly-based Intrusion Detection in Industrial Data with SVM and Random Forests," arXiv preprint arXiv:1907.10374, 2019.
[27] I. G. A. Poornima and B. Paramasivan, "Anomaly detection in wireless sensor network using machine learning algorithm," Computer communications, vol. 151, pp. 331-337, 2020.
[28] G. G. Gebremariam, J. Panda, and S. Indu, "Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks," Connection Science, vol. 35, no. 1, p. 2246703, 2023.
[29] S. Ho, S. Al Jufout, K. Dajani, and M. Mozumdar, "A novel intrusion detection model for detecting known and innovative cyberattacks using convolutional neural network," IEEE Open Journal of the Computer Society, vol. 2, pp. 14-25, 2021.
[30] J. Yu, X. Ye, and H. Li, "A high precision intrusion detection system for network security communication based on multi-scale convolutional neural network," Future Generation Computer Systems, vol. 129, pp. 399-406, 2022.
[31] B. Almaslukh, "Deep Learning and Entity Embedding-Based Intrusion Detection Model for Wireless Sensor Networks," Computers, Materials & Continua, vol. 69, no. 1, 2021.
[32] V. Gowdhaman and R. Dhanapal, "An intrusion detection system for wireless sensor networks using deep neural network," Soft Computing, pp. 1-9, 2021.
[33] T. Gopala, V. Raviram, and U. K. NL, "Detecting Security Threats in Wireless Sensor Networks using Hybrid Network of CNNs and Long Short-Term Memory," International Journal of Intelligent Systems and Applications in Engineering, vol. 12, no. 1s, pp. 704-722, 2024.
[34] V. Gatate and J. Agarkhed, "Enhancing Intrusion Detection in Wireless Sensor Networks through Deep Hybrid Network Empowered by SC-Attention Mechanism," 2023.
[35] M. Safaldin, M. Otair, and L. Abualigah, "Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks," Journal of ambient intelligence and humanized computing, vol. 12, pp. 1559-1576, 2021.
[36] D. Hemanand, G. V. Reddy, S. S. Babu, K. R. Balmuri, T. Chitra, and S. Gopalakrishnan, "An intelligent intrusion detection and classification system using CSGO-LSVM model for wireless sensor networks (WSNs)," International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 3, pp. 285–293-285–293, 2022.
[37] M. Asif, S. Abbas, M. Khan, A. Fatima, M. A. Khan, and S.-W. Lee, "MapReduce based intelligent model for intrusion detection using machine learning technique," Journal of King Saud University-Computer and Information Sciences, 2021.
[38] M. Ahsan and K. E. Nygard, "Convolutional Neural Networks with LSTM for Intrusion Detection," in CATA, 2020, vol. 69, pp. 69-79.
[39] B. Riyaz and S. Ganapathy, "A deep learning approach for effective intrusion detection in wireless networks using CNN," Soft Computing, vol. 24, pp. 17265-17278, 2020.
[40] L. Zhiqiang, G. Mohiuddin, Z. Jiangbin, M. Asim, and W. Sifei, "Intrusion detection in wireless sensor network using enhanced empirical based component analysis," Future Generation Computer Systems, vol. 135, pp. 181-193, 2022.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2024 EAI Endorsed Transactions on Internet of Things
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 3.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
