Intelligent water drop algorithm and hopfield neural network for effective botnet detection

Botnet detection remains a critical challenge in cybersecurity due to the evolving nature of malicious networks. This paper presents a novel approach for detecting botnets by integrating the Intelligent Water Drop (IWD) algorithm with the Hopfield Neural Network (HNN). The IWD algorithm, inspired by the natural flow of water drops in rivers, is employed to optimize the feature selection process, reducing the dimensionality of network traffic data while preserving essential characteristics for accurate botnet detection. Hopfield Neural Network is then applied to classify the refined features, leveraging its associative memory capabilities for efficient detection. Experimental results demonstrate that the proposed IWDHNN framework significantly improves detection accuracy reaching 94%. This hybrid approach offers a promising solution for enhancing the security of network systems against botnet threats.