Evolving Computational Intelligence System for Malware Detection

Recent malware developments have the ability to remain hidden during infection and operation. They prevent analysis and removal, using various techniques, namely: obscure filenames, modification of file attributes, or operation under the pretense of legitimate programs and services. Also, the malware might attempt to subvert modern detection software, by hiding running processes, network connections and strings with malicious URLs or registry keys. The malware can go a step further and obfuscate the entire file with a packer, which is special software that takes the original malware file and compresses it, thus making all the original code and data unreadable. This paper proposes a novel approach, which uses minimum computational power and resources, to indentify Packed Executable (PEX), so as to spot the existence of malware software. It is an Evolving Computational Intelligence System for Malware Detection (ECISMD) which performs classification by Evolving Spiking Neural Networks (eSNN), in order to properly label a packed executable. On the other hand, it uses an Evolving Classification Function (ECF) for the detection of malwares and applies Genetic Algorithms to achieve ECF Optimization.