Why would anomaly detection IDSs often generate a large number of false positives?
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Unfortunately, anomaly detectors and the Intrusion Detection Systems (IDS) based on them often produce a large number of false alarms, as normal patterns of user and system behavior can vary wildly.
Being only able to identify correctly attacks they already know about is a characteristic of misuse detection (signature- based) IDSs.
Application-based IDSs are a special subset of host-based IDSs that analyze the events transpiring within a software application.
They are more vulnerable to attacks than host-based IDSs.
Not being able to identify abnormal behavior would not cause false positives, since they are not identified.
Source: DUPUIS, Cl?ment, Access Control Systems and Methodology CISSP Open Study Guide, version 1.0, march 2002 (page 92).
Anomaly detection intrusion detection systems (IDSs) are designed to identify patterns of behavior that are not consistent with normal behavior. The purpose of anomaly detection is to identify threats that cannot be identified using signature-based IDSs, which rely on known patterns of behavior to detect threats.
However, anomaly detection IDSs often generate a large number of false positives for several reasons:
Limited knowledge: Anomaly detection IDSs can only identify correctly attacks they already know about. If a new type of attack is encountered, the IDS may not be able to identify it as abnormal behavior and may mistakenly classify it as normal behavior.
Application-based attacks: Anomaly detection IDSs are more subject to attacks targeting specific applications. Attackers can manipulate the behavior of a specific application to avoid detection by the IDS, leading to false positives.
Inability to identify abnormal behavior: Anomaly detection IDSs can be easily fooled by legitimate activity that deviates from normal behavior, such as a user accessing a resource they have never accessed before. This can lead to false positives if the IDS does not have enough context to understand the behavior.
Normal behavior variations: Normal patterns of user and system behavior can vary widely, depending on the time of day, day of the week, and other factors. Anomaly detection IDSs can generate false positives if they mistake normal behavior variations for abnormal behavior.
Overall, while anomaly detection IDSs can be effective in detecting unknown threats, they need to be carefully tuned to avoid generating a large number of false positives. This requires careful analysis of the system being monitored and a thorough understanding of the normal patterns of behavior.