Intelligent Intrusion Detection using Artificial Bee Colony-Based Rule Discovery Techniques

Network protection demands intrusion Detection Systems (IDS) because they detect malicious activities along with blocking unauthorized access to sensitive networks. The detection methods of old-style IDS which contain signature-based and anomaly-based detection produce high numbers of false positives as well as complicated computational requirements. The proposed Intelligent Intrusion Detection System uses Artificial Bee Colony rule discovery methods for intrusion optimization in security applications. The integration of swarm intelligence with heuristic optimization forms an approach to boost IDS efficiency through continuous rule classification optimization. The rule discovery adaptation features of ABC optimization help solve the local optimality problems that exist in regular algorithms. An IDS operates through four core sections: data preprocessing along with feature selection as well as rule discovery and attack classification. Resulting from the implementation of ABC method the classifier shows enhanced precision which minimizes false alerts without losing detection efficiency. The proposed method achieves successful results in experimental trials which utilize benchmark datasets KDD and NSL-KDD. The research shows that swarm-based artificial intelligence serves as an opportunity to build stronger intrusion detection frameworks which will establish resilient adaptive cybersecurity frameworks.