Hybrid Neural Network Model for Protection of Dynamic Cyber Infrastructure

Abstract

The paper considers a combination of modern artificial neural networks (ANN) that solves the security relative task of intrusion prevention and vulnerabilities detection in cybernetic infrastructure with dynamic network topology. Self-organizing networks, WSN, m2m networks, IIoT, mesh networks are faced with the cyberthreats of specific character: dynamic routing failures, node isolation, DDoS attacks, traffic lack, etc. Most of them are caused by cybersecurity weaknesses: the software vulnerabilities and architectural features of dynamically reconfigured network. The existing methods of binary code analysis and intrusion detection can work with a small number of data sets, are designed for either code inspection or network checking, and are targeted for static networks with regular topology. The proposed neural model demonstrates an universal approach that deals with the cybersecurity weakness as a systems genuine property and attempts to approximate it using a hybrid deep ANN. The new ANN detects both the network security defects and binary code vulnerabilities at once with high accuracy (more than 0.97). It also shows good performance capacity processing big data of the undercontrolled network.