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Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis.

Authors :
Rao, Deepak Dasaratha
Waoo, Akhilesh A.
Singh, Murlidhar Prasad
Pareek, Piyush Kumar
Kamal, Shoaib
Pandit, Shraddha V.
Source :
Journal of Intelligent Systems & Internet of Things; 2024, Vol. 12 Issue 2, p195-207, 13p
Publication Year :
2024

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2769786X
Volume :
12
Issue :
2
Database :
Complementary Index
Journal :
Journal of Intelligent Systems & Internet of Things
Publication Type :
Academic Journal
Accession number :
179114795
Full Text :
https://doi.org/10.54216/JISIoT.120215