2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 12 , Issue 2 , PP: 195-207, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Deepak Dasaratha Rao 1 , Akhilesh A. Waoo 2 , Murlidhar Prasad Singh 3 , Piyush Kumar Pareek 4 * , Shoaib Kamal 5 , Shraddha V. Pandit 6
Doi: https://doi.org/10.54216/JISIoT.120215
This research introduces an algorithmic framework for enhancing the security of Internet of Things (IoT) networks. The Enhanced Anomaly Detection (EAD) algorithm initiates the process by detecting anomalies in real-time IoT data, serving as the foundational layer. The Behavior Analysis for Profiling (BAP) algorithm builds upon EAD, adding behavior analysis for profiling and adaptive identification of abnormal behavior. Signature-Based Detection (SBD) involves pre-identified attack signatures, which supports detection of known attacks and provides proactive defense measures against documented threats. The MLID, or the Machine Learning-Based Intrusion Detection, algorithm uses trained machine learning models in order to detect anomalies and the adaptability to changing security risks. The Real-Time Threat Intelligence Integration (RTI) algorithm integrates updated threat intelligence feeds, which improves the framework's responsiveness to emerging threats. The visual representations illustrate once again the idea of the new framework being very accurate at intergration, applicability, and overal security effectiveness. The research makes a standard solution which proves to be a smart and responsive way guarding the IoT networks reducing and even fighting known and potential threats in a real-time mode.
Anomaly Detection , Behavior Analysis , Dynamic Threshold Adjustment , IoT Security , Machine Learning-Based Intrusion Detection , Real-Time Threat Intelligence Integration , Robust Detection , Signature-Based Detection , Training Data.
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