Which of the following is an issue with signature-based intrusion detection systems?
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A. B. C. D.A.
An issue with signature-based ID is that only attack signatures that are stored in their database are detected.
New attacks without a signature would not be reported.They do require constant updates in order to maintain their effectiveness.
Reference used for this question: KRUTZ, Ronald L.
& VINES, Russel.
D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 49.
Signature-based intrusion detection systems (IDS) are designed to identify malicious activities on a network or system by matching known attack signatures or patterns against incoming network traffic or system logs. These signatures are usually based on the characteristics of known threats, such as specific network packets, protocol violations, or patterns of behavior associated with a particular malware.
The primary issue with signature-based IDS is that they can only detect previously identified attack signatures. In other words, if an attacker uses a new or modified technique that does not match any existing signature in the IDS database, the attack may go undetected. This makes signature-based IDS less effective in detecting new or sophisticated attacks that have not been previously observed and recorded.
Another limitation of signature-based IDS is that they can generate a high number of false positives or false negatives. False positives occur when the IDS mistakenly identifies legitimate traffic as malicious, while false negatives occur when the IDS fails to detect actual attacks. These false positives and false negatives can lead to wasted resources and missed opportunities to detect and respond to actual threats.
Additionally, maintaining a comprehensive and up-to-date signature database can be a challenging and time-consuming task. Signature databases must be constantly updated with new signatures to keep up with the evolving threat landscape. Moreover, attackers can easily circumvent signature-based IDS by using techniques such as polymorphism or obfuscation to modify their attacks and avoid detection.
To address these issues, IDS vendors have introduced complementary techniques such as behavioral-based analysis, anomaly detection, and machine learning to improve the accuracy and effectiveness of IDS. These techniques can identify previously unknown attacks and adapt to new threats without relying solely on a signature database.