Congnitive Networks, Their Properties and Applications in Attack Detection and Prevention Systems

The paper considers real-time cyberattack detection methods based on an algebraic approach. The author has applied the algebraic matching method, which relies on solving behavioral equations and algebraic modeling. The paper proposes to improve the efficiency of attack detection and prevention by combining a neural network that classifies attacks with an algebraic matching method. This scheme is called a cognitive network, the concept of which was first conceived in 2005. The cognitive network properties, such as double and self-learning, are discussed. The technological line for cyberattack detection using cognitive networks is presented.