Enhancing IoT Security: Machine Learning Strategies for Intrusion Detection in Connected Networks

EasyChair Preprint 12038

Abstract

The proliferation of Internet of Things (IoT) devices has led to an unprecedented increase in the complexity and scale of network environments, posing significant challenges to security. Intrusion Detection Systems (IDS) play a crucial role in safeguarding IoT networks from malicious activities. This paper explores the application of machine learning (ML) approaches for enhancing intrusion detection in IoT networks. Various ML algorithms are investigated for their effectiveness in identifying anomalous patterns and potential threats in real-time, providing a proactive defense mechanism against evolving cyber threats in IoT ecosystems.

Keyphrases: Cyber Threats, Internet of Things, Intrusion Detection, IoT networks, Security, anomaly detection, classification algorithms, machine learning, supervised learning, unsupervised learning

@booklet{EasyChair:12038,
  author    = {Usman Hider},
  title     = {Enhancing IoT Security: Machine Learning Strategies for Intrusion Detection in Connected Networks},
  howpublished = {EasyChair Preprint 12038},
  year      = {EasyChair, 2024}}