Fortifying IoT Security: Harnessing Machine Learning for Enhanced Intrusion Detection in Interconnected Networks

EasyChair Preprint no. 12373

Abstract

As the Internet of Things (IoT) continues to proliferate, ensuring robust security measures becomes paramount. This paper explores the integration of machine learning strategies for enhancing intrusion detection in interconnected networks. By leveraging advanced algorithms, anomaly detection, and behavioral analysis, the proposed approach aims to fortify IoT security and mitigate emerging threats. The study evaluates the effectiveness of these machine learning techniques in identifying and responding to unauthorized access, malicious activities, and potential vulnerabilities within connected ecosystems.

Keyphrases: anomaly detection, behavioral analysis, Connected Networks, Cybersecurity, Intrusion Detection, IoT Security, machine learning, Threat Mitigation, unauthorized access, Vulnerability

@Booklet{EasyChair:12373,
  author = {Usman Hider},
  title = {Fortifying IoT Security: Harnessing Machine Learning for Enhanced Intrusion Detection in Interconnected Networks},
  howpublished = {EasyChair Preprint no. 12373},

  year = {EasyChair, 2024}}