Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{202930,
        author = {K Vishwa and Dr. P. Poornima and G. Sandeep},
        title = {A Multi-Level Machine Learning–Based Intrusion Detection System for IoT Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8716-8726},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202930},
        abstract = {The rapid growth of Internet of Things (IoT) devices has introduced a new range of security risks, making networks increasingly vulnerable to brute force, Distributed Denial of Service (DDoS), malware, and lateral movement attacks. Traditional signature-based Intrusion Detection Systems (IDS) are inadequate for dynamic IoT environments, particularly when encrypted communication occurs. This paper proposes a multi-level machine learning–based intrusion detection framework for IoT networks, operating in real time. The framework combines anomaly detection, behavioral analysis, and rule-based detection with a fusion-based scoring mechanism to improve accuracy and reduce false alarms. A Security Operations Center (SOC) dashboard, built using Streamlit, is integrated for real-time monitoring, device discovery, automated alert generation, and threat prioritization. Validation in a virtualized laboratory environment using a dataset of 211,043 records demonstrates high detection accuracy with low computational overhead, confirming suitability for real-world IoT security deployment.},
        keywords = {Cyber threats detection, flow-based analysis, intrusion detection system, IoT security, machine learning, network traffic monitoring, Security Operations Center (SOC).},
        month = {May},
        }