The Gap in Intrusion Detection: How SnortML Fills the Void In recent years, intrusion detection systems (IDS) have become increasingly reliant on traditional signature based approaches to identify and prevent attacks.
However, these methods often fall short when faced with novel or evasive threats that exploit vulnerabilities not yet accounted for by human written rules.
The gap between the specificity of signatures and the adaptability of attackers has long been a challenge in IDS deployments, leading to false negatives and missed opportunities for detection.