(untitled) (Cybersecurity and Network Defense Research, 2024)
Intrusion Detection Systems (IDS) play a crucial role in safeguarding computer networks against cyber threats by monitoring and analyzing network traffic for suspicious activities. This paper provides an overview of techniques for building and evaluating IDS. We discuss various types of IDS, including signature-based, anomaly-based, and hybrid IDS, along with their strengths and limitations. Furthermore, we examine the importance of dataset selection, feature extraction, and machine learning algorithms in designing effective IDS. Evaluation metrics and methodologies for assessing the performance of IDS are also discussed. The paper concludes with future research directions and challenges in the field of intrusion detection.