What is the difference between the rule-based detection when compared to behavioral detection?

Rule-based detection, also known as signature-based detection, operates using a static set of predefined rules or patterns that correspond to known malicious activities. These rules do not change unless manually updated by an administrator or vendor. In contrast, behavioral detection, or anomaly-based detection, first establishes a baseline of normal network or system activity. It then monitors for deviations from this established baseline. This baseline can adapt and change over time as the system "learns" new normal patterns, making it a dynamic detection method capable of identifying previously unseen threats.

A. This is incorrect because behavioral detection identifies deviations from a normal baseline, whereas signature-based (rule-based) detection identifies threats per signature.
C. This misrepresents both systems. Behavioral systems learn a baseline of normal activity, they are not based on "predefined patterns from hundreds of users." Rule-based systems flag known malicious signatures, not just "potentially abnormal patterns."
D. This option incorrectly swaps the definitions. Finding sequences that match a particular attack signature is the function of rule-based systems, not behavioral systems.

1. Cisco Press. (2020). Cisco CyberOps Associate CBROPS 200-201 Official Cert Guide. Cisco Press. In Chapter 13, "Security Monitoring," the text distinguishes between signature-based (rule-based) and anomaly-based (behavioral) detection. It explains that signature-based systems use a "database of signatures of known malicious activity" which are static patterns, while anomaly-based systems "first define a baseline of normal network and host activity" and can adapt to changes.

2. National Institute of Standards and Technology (NIST). (2007). Guide to Intrusion Detection and Prevention Systems (IDPS). (NIST Special Publication 800-94). Section 2.2.1, "Signature-Based Detection," states that this method uses "specific patterns (signatures) for known threats" and that "new attacks must have a new signature created for them." Section 2.2.2, "Anomaly-Based Detection," describes it as comparing "definitions of what is considered normal activity against observed events to identify significant deviations," highlighting its dynamic and baseline-dependent nature.

3. Denning, D. E. (1987). An Intrusion-Detection Model. IEEE Transactions on Software Engineering, SE-13(2), 222–232. https://doi.org/10.1109/TSE.1987.232894. This foundational academic paper introduces the concept of anomaly-based (behavioral) detection, describing it as a system that maintains profiles of normal subject behavior and flags statistically significant deviations, contrasting it with methods that use fixed rules for known intrusion scenarios.