A Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security

Konstantinos Demertzis () and Lazaros Iliadis ()
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Konstantinos Demertzis: Democritus University of Thrace, Department of Forestry & Management of the Environment & Natural Resources
Lazaros Iliadis: Democritus University of Thrace, Department of Forestry & Management of the Environment & Natural Resources

A chapter in Computation, Cryptography, and Network Security, 2015, pp 161-193 from Springer

Abstract: Abstract Confidentiality, Integrity, and Availability of Military information is a crucial and critical factor for a country’s national security. The security of military information systems (MIS) and Networks (MNET) is a subject of continuous research and design, due to the fact that they manage, store, manipulate, and distribute the information. This study presents a bio-inspired hybrid artificial intelligence framework for cyber security (bioHAIFCS). This framework combines timely and bio-inspired Machine Learning methods suitable for the protection of critical network applications, namely military information systems, applications and networks. More specifically, it combines (a) the hybrid evolving spiking anomaly detection model (HESADM), which is used in order to prevent in time and accurately, cyber-attacks, which cannot be avoided by using passive security measures, namely: Firewalls, (b) the evolving computational intelligence system for malware detection (ECISMD) that spots and isolates malwares located in packed executables untraceable by antivirus, and (c) the evolutionary prevention system from SQL injection (ePSSQLI) attacks, which early and smartly forecasts the attacks using SQL Injections methods.

Keywords: Genetic Algorithm; Artificial Neural Network; Intrusion Detection System; Minority Class; Malicious Code (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-319-18275-9_7

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