Volume 13 , Issue 1 , PP: 60-68, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Aziz 1 * , Sanjar Mirzaliev 2
Doi: https://doi.org/10.54216/JCIM.130106
The ubiquity of interconnected devices within the Internet of Things (IoT) paradigm has revolutionized modern connectivity, simultaneously amplifying the susceptibility of networks to diverse security threats. This study addresses the pressing necessity for robust intrusion detection mechanisms tailored for IoT networks. Utilizing a simulated dataset reflecting a spectrum of network intrusions within a military environment, the research employs sophisticated methodologies, notably harnessing Decision Tree (DT) algorithms optimized via Grey Wolf Optimization (GWO) for hyperparameter tuning. The investigation meticulously evaluates and refines intrusion detection mechanisms, emphasizing the pivotal role of feature importance analysis in fortifying network security. Results demonstrate the efficacy of the optimized DT algorithm in the precise classification of network traffic, illuminating key attributes instrumental for intrusion detection. These findings underscore the significance of adaptive and interpretable detection strategies in mitigating evolving threats within IoT networks, advocating for resilient approaches to bolster network security.
Intrusion Detection , Internet of Things (IoT) , Network Security , Cybersecurity Measures , Threat Detection , Security Frameworks , Intrusion Mitigation , IoT Devices , Cyber Threats , Network Vulnerabilities , Security Optimization , Anomaly Detection , Machine Learning
[1] Chaabouni, Nadia, Mohamed Mosbah, Akka Zemmari, Cyrille Sauvignac, and Parvez Faruki. 2019. “Network Intrusion Detection for IoT Security Based on Learning Techniques.” IEEE Communications Surveys and Tutorials. https://doi.org/10.1109/COMST.2019.2896380.
[2] Otoum, Yazan, Dandan Liu, and Amiya Nayak. 2022. “DL-IDS: A Deep Learning–Based Intrusion Detection Framework for Securing IoT.” Transactions on Emerging Telecommunications Technologies. https://doi.org/10.1002/ett.3803.
[3] Kan, Xiu, Yixuan Fan, Zhijun Fang, Le Cao, Neal N Xiong, Dan Yang, and Xuan Li. 2021. “A Novel IoT Network Intrusion Detection Approach Based on Adaptive Particle Swarm Optimization Convolutional Neural Network.” Information Sciences 568: 147–62.
[4] Liu, Jingyu, Dongsheng Yang, Mengjia Lian, and Mingshi Li. 2021. “Research on Intrusion Detection Based on Particle Swarm Optimization in IoT.” IEEE Access 9: 38254–68.
[5] Fatani, Abdulaziz, Mohamed Abd Elaziz, Abdelghani Dahou, Mohammed A A Al-Qaness, and Songfeng Lu. 2021. “IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization.” IEEE Access 9: 123448–64.
[6] Luo, Ke. 2023. “A Distributed SDN-Based Intrusion Detection System for IoT Using Optimized Forests.” Plos One 18 (8): e0290694.
[7] Fatani, Abdulaziz, Abdelghani Dahou, Mohammed A A Al-Qaness, Songfeng Lu, and Mohamed Abd Elaziz. 2021. “Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System.” Sensors 22 (1): 140.
[8] Yang, Aimin, Yunxi Zhuansun, Chenshuai Liu, Jie Li, and Chunying Zhang. 2019. “Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network.” Ieee Access 7: 106043–52.
[9] Ramaiah, Mangayarkarasi, Vanmathi Chandrasekaran, Vinayakumar Ravi, and Neeraj Kumar. 2021. “An Intrusion Detection System Using Optimized Deep Neural Network Architecture.” Transactions on Emerging Telecommunications Technologies 32 (4): e4221.
[10] A. Metwaly, A. and Elhenawy, I. (2023) “Protecting IoT Devices from BotNet Threats: A Federated Machine Learning Solution”, Sustainable Machine Intelligence Journal, 2. doi: 10.61185/SMIJ.2023.22105.
[11] Liang, Wei, Kuan-Ching Li, Jing Long, Xiaoyan Kui, and Albert Y Zomaya. 2019. “An Industrial Network Intrusion Detection Algorithm Based on Multifeature Data Clustering Optimization Model.” IEEE Transactions on Industrial Informatics 16 (3): 2063–71.
[12] Nazir, Anjum, and Rizwan Ahmed Khan. 2021. “A Novel Combinatorial Optimization Based Feature Selection Method for Network Intrusion Detection.” Computers \& Security 102: 102164.
[13] Savanović, Nikola, Ana Toskovic, Aleksandar Petrovic, Miodrag Zivkovic, Robertas Damaševičius, Luka Jovanovic, Nebojsa Bacanin, and Bosko Nikolic. 2023. “Intrusion Detection in Healthcare 4.0 Internet of Things Systems via Metaheuristics Optimized Machine Learning.” Sustainability 15 (16): 12563.
[14] Aziz, Mohammad R, and Ali Saeed Alfoudi. 2023. “Different Mechanisms of Machine Learning and Optimization Algorithms Utilized in Intrusion Detection Systems.” In AIP Conference Proceedings. Vol. 2839.
[15] Roy, Souradip, Juan Li, Bong-Jin Choi, and Yan Bai. 2022. “A Lightweight Supervised Intrusion Detection Mechanism for IoT Networks.” Future Generation Computer Systems 127: 276–85.
[16] Phalguna Krishna, Ediga Sathyanarayana, and Thangavelu Arunkumar. 2021. “Hybrid Particle Swarm and Gray Wolf Optimization Algorithm for IoT Intrusion Detection System.” International Journal of Intelligent Engineering \& Systems 14 (4).
[17] Kunang, Yesi Novaria, Siti Nurmaini, Deris Stiawan, and Bhakti Yudho Suprapto. 2021. “Attack Classification of an Intrusion Detection System Using Deep Learning and Hyperparameter Optimization.” Journal of Information Security and Applications 58: 102804.
[18] Kavitha, S, N Uma Maheswari, and R Venkatesh. 2023. “Intelligent Intrusion Detection System Using Enhanced Arithmetic Optimization Algorithm with Deep Learning Model.” Tehnički Vjesnik 30 (4): 1217–24.
[19] Kumar, Ravinder, Amita Malik, and Virender Ranga. 2022. “An Intellectual Intrusion Detection System Using Hybrid Hunger Games Search and Remora Optimization Algorithm for IoT Wireless Networks.” Knowledge-Based Systems 256: 109762.
[20] Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., & Ryan, M. J. (2021). Semi-supervised spatiotemporal deep learning for intrusions detection in IoT networks. IEEE Internet of Things Journal
