Utilizing Machine Learning for Cybersecurity: Techniques in Intrusion Detection

EasyChair Preprint 12015

Abstract

In the realm of cybersecurity, the detection of intrusions is paramount for safeguarding networks against malicious activities. Traditional rule-based approaches have limitations in detecting sophisticated and evolving threats. Consequently, machine learning techniques have gained prominence due to their ability to adapt and learn from data patterns to identify anomalies indicative of intrusions. This paper explores various machine learning methods employed in intrusion detection systems, including supervised, unsupervised, and semi-supervised approaches. Furthermore, it discusses the challenges associated with implementing machine learning in cybersecurity and highlights avenues for future research to enhance the effectiveness and efficiency of intrusion detection mechanisms.

Keyphrases: Cybersecurity, Intrusion Detection, Network Security, anomaly detection, machine learning, semi-supervised learning, supervised learning, unsupervised learning

@booklet{EasyChair:12015,
  author    = {Haney Zaki},
  title     = {Utilizing Machine Learning for Cybersecurity: Techniques in Intrusion Detection},
  howpublished = {EasyChair Preprint 12015},
  year      = {EasyChair, 2024}}