Ensemble Learning-Based Intrusion Detection and Classification for Securing IoT Networks: An Optimized Strategy for Threat Detection and Prevention

Journal of Intelligent Systems and Internet of Things JISIoT 2690-6791 2769-786X 10.54216/JISIoT https://www.americaspg.com/journals/show/3883 2019 2019 Ensemble Learning-Based Intrusion Detection and Classification for Securing IoT Networks: An Optimized Strategy for Threat Detection and Prevention Associate Professor, Dept. of CSE (AI&ML), Sai Vidya Institute of Technology, Visvesvaraya Technological University, Bengaluru, Karnataka, India Sanjay Sanjay Associate Professor, Dept. of ISE, Shridevi Institute of Engineering and Technology, Visvesvaraya Technological University, Karnataka, India Charan K.. V. Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, AP, India B. Yamini Supriya Assistant Professor, School of Computer Science and Engineering, Ramdeobaba University, Nagpur, Maharashtra, India Purshottam J. Assudani Principal, MIT Muzaffarpur, Bihar, India Chandra Bhushan Mahato Professor, Dept. of AI&DS, Sri Shanmugha College of Engineering and Technology And Director Research, Sri Shanmugha Educational Institutions, Sankari, Salem, TN, India Sanjay Kumar Suman The Internet of Things (IoT) advancement has created new security holes, which require intrusion detection systems to defend networks effectively. The complex structure of IoT networks causes traditional security methods to fail because they produce high amounts of incorrect detections and limited ability to accurately identify threats. The authors introduce ID-ELC: Ensemble Learning and Classification framework for Intrusion Detection, which aims to strengthen IoT environment security. A new ID-ELC model uses CS optimization with composite variance to choose network features that boost their detection capabilities. The cybersecurity evaluation of the system utilized Kyoto network records that included 91,000 intrusion-prone records and 59,000 benign logs from 150,000 total records. Experiments revealed ID-ELC surpasses Statistical Flow Features (SFF) and Two-layer Dimension Reduction and Two-tier Classification (TDRTC) through precision 0.98, accuracy 0.98, sensitivity 0.99 and specificity 0.97. Science-based evaluations confirm ID-ELC represents a flexible and resilient tool for IoT intrusion protection that shows practical value for citywide security systems and medicine networks and manufacturing operations. Future investigation will concentrate on enhancing the selection of features alongside classification methods to address rising cyber threats. 2025 2025 101 118 10.54216/JISIoT.170208 https://www.americaspg.com/articleinfo/18/show/3883