An Intrusion Detection System Based on Hybridized Firefly and Artificial Bee Colony Optimization Algorithms
DOI:
https://doi.org/10.33003/fjs-2026-10(ANB-K)-5306Keywords:
Intrusion Detection System, Firefly Algorithm, Artificial Bee Colony, Swarm Intelligence, Cyber securityAbstract
Intrusion Detection Systems (IDS) are fundamental to safeguarding network infrastructures against evolving cyber threats; however, traditional detection techniques often face challenges associated with high-dimensional datasets, limited adaptability, and suboptimal detection accuracy. This study presents a hybrid intrusion detection framework that combines the Firefly Algorithm (FA) and Artificial Bee Colony (ABC) optimization algorithm to improve detection performance through optimized feature selection and reduced false alarm rates. The NSL-KDD dataset was preprocessed by eliminating irrelevant attributes and normalizing input features to enhance data consistency and analytical efficiency. The proposed hybrid FA-ABC approach exploits the local search capability of the FA and the global exploration strength of the ABC algorithms to achieve effective intrusion detection. Performance evaluation was conducted using different data partitioning ratios, including 80:20, 70:30, 75:25, and 65:35. Experimental results demonstrated that the hybrid framework consistently outperformed the standalone FA and ABC approaches across key performance indicators. Among the evaluated partitions, the 80:20 ratio achieved the highest Area under Receiver Operating Characteristic (AUC-ROC) score of 0.90, indicating superior classification accuracy and stable convergence characteristics. Despite these improvements, the 75:25 partition exhibited extremely low population diversity, reflecting reduced adaptability under certain optimization conditions. This limitation highlights the necessity for future enhancement strategies aimed at preserving diversity during optimization. Overall, the proposed framework provides an efficient and reliable IDS suitable for real-time cybersecurity applications and offers potential for future integration with deep learning-based security systems.
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