Cognitive clustering algorithm for efficient cybersecurity applications

Cyber security is an important issue in today´s global computer networks. Advanced clustering methods are relevant for efficient data mining over the web. KIII is a biologically plausible neural network model. In its multi-layer architecture there are excitatory and inhibitory neurons, which present lateral, feedforward, and delayed feedback connections between layers in a massive way. KIII has been successfully employed in classification and pattern recognition tasks. In this work we develop a methodology to use KIII for community detection. It is shown that clustering methods that employ KIII related to cybersecurity achieve better results, despite the amount of data available by such application.