Intrusion Detection Landscape: Exploring Progress and Confronting Challenges in Security Advances

In the face of escalating cyber-attacks marked by increasing sophistication, the accurate detection of intrusions has become a paramount challenge in the realm of computer security. Failure to prevent these intrusions can have far-reaching consequences, affecting data confidentiality, integrity, and availability, ultimately eroding the trust in security services. This comprehensive survey explores the intricate landscape of intrusion detection methods, a cornerstone of defense against cybersecurity threats. The paper categorizes intrusion detection methods into two main types: Signature-based Intrusion Detection Systems (SIDS) and Anomaly-based Intrusion Detection Systems (AIDS). With meticulous detail, the paper presents a taxonomy of contemporary IDS, providing a systematic understanding of their diverse approaches. Additionally, the paper conducts a thorough review of recent ground breaking works, highlighting the cutting-edge advancements in intrusion detection techniques. Moreover, the paper delves into the crucial role of datasets commonly used for evaluation, emphasizing their impact on the effectiveness of intrusion detection systems. Beyond the current state of IDS, the paper critically examines the evasion techniques employed by attackers to evade detection, emphasizing the need for robust countermeasures. Finally, the paper contemplates the future of intrusion detection, outlining the research challenges that lie ahead. These challenges serve as essential focal points for strengthening computer systems and enhancing their resilience against evolving cyber threats.