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

EasyChair Preprint no. 12807

Abstract

With the rapid proliferation of Internet of Things (IoT) devices, ensuring robust security measures within connected networks has become imperative. Traditional security approaches are often insufficient to combat the evolving threats in IoT environments. This paper proposes leveraging machine learning techniques for enhanced intrusion detection in IoT networks. By harnessing the power of machine learning algorithms, such as deep learning and anomaly detection, we aim to detect and mitigate potential intrusions effectively. This research explores various aspects of implementing machine learning-based intrusion detection systems tailored to IoT environments, including data preprocessing, feature selection, model training, and real-time monitoring. Through experimentation and analysis, we demonstrate the efficacy of our proposed approach in detecting both known and unknown threats, thereby strengthening the overall security posture of IoT networks

Keyphrases: anomaly detection, Connected Networks, data preprocessing, deep, detection, feature selection, Intrusion Detection, IoT Security, learning, machine learning, real-time monitoring, threat

@Booklet{EasyChair:12807,
  author = {Roni Joni},
  title = {Enhancing IoT Security: Utilizing Machine Learning for Advanced Intrusion Detection in Connected Networks},
  howpublished = {EasyChair Preprint no. 12807},

  year = {EasyChair, 2024}}