Table 2.

Comparison of ID methods.

Feature	Signature-Based IDS [70]	Honeypot [71]	Machine Learning-Based IDS [72]	Integrated Machine Learning and Signature-Based IDS with Honeypot (Proposed Model)
Detection Method	Relies on pre-defined signatures to match known attack patterns.	Lures attackers into a decoy system mimicking real network services to observe their behavior.	Learns from network traffic data to identify patterns indicative of attacks.	Combines machine learning for pattern recognition with signature-based detection for known threats. Honeypot lures attackers to gather further intel.
Strength	-Fast and efficient. -Lower false positives (with well-established signatures). -Explainable decisions based on matched signatures.	-Detects zero-day attacks and Advanced Persistent Threats (APTs). Provides rich attacker behavior data for analysis. -Can be used for attacker profiling and deception.	-Adaptable to novel attacks. -Continuous learning improves detection accuracy. -Can automatically identify relevant features.	-Adaptable to novel attacks with machine learning. -Faster detection with a signature-based approach. -Rich attacker behavior data from the honeypot -Provides insights into attacker techniques and tools.
Considerations	-Limited adaptability to unseen attacks. -Requires constant signature updates to stay effective. -Evasion techniques can bypass signature-based detection. -Generally simpler to deploy and manage.	-Requires careful configuration to mimic real systems effectively. -Limited scalability for large deployments. -Potential security risks if compromised. -Requires careful configuration and isolation to avoid compromising real systems. Expertise in honeypot analysis is essential.	-Computationally expensive (training and running models). -Susceptible to false positives due to model biases or data limitations. -Black box nature: decision-making process might be less interpretable. -Generally, more complex, requiring expertise for setup, configuration, and maintenance.	-Increased complexity in deployment and maintenance. -Requires expertise in both machine learning and honeypot analysis. -Potential for false positives due to model biases or data limitations.