An IDS-based Adaptive Neural Fuzzy Inference System (ANFIS) for IoBT Security Utilizing Particle Swarm Optimization

Within the domain of cybersecurity, Intrusion Detection Systems (IDSs) are crucial to protecting network infrastructures from hostile actions. This paper presents a novel approach that leverages Particle Swarm Optimization (PSO) for feature selection to enhance the performance of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based IDS. The PSO algorithm identifies the most significant features of a dataset, reducing dimensionality and computational complexity. Subsequently, the selected features are utilized by the ANFIS model to detect and classify intrusions with greater accuracy and efficiency. The proposed PSO-ANFIS framework outperforms traditional methods in terms of detection accuracy and false positive rate, as evidenced by experimental results. The combination of PSO for feature selection with ANFIS for intrusion detection offers a solution to cybersecurity issues, especially in dynamic and intricate systems such as the Internet of Battlefield Things (IoBT).