Malware Analysis Using Modified Genetic Algorithm in Cyber-Physical Systems

The integration of communication networks and the Internet of Things (IoT) into Cyber-Physical Systems (CPSs) is the reason for increased vulnerability in terms of cyber attacks. Cyber-Physical Systems (CPSs) is highly important to protect critical information and detect cyber threats. These new forms of threat come without any prebuilt signature to detect them. The proposed work aims to solve this problem by using machine learning techniques by performing dynamic malware analysis for windows executable files. Genetic Programming is used for selecting malicious features from benign files after extracting them with the help of the Cuckoo apparatus. These selected features are used to train machine learning classifiers. Later, these classifiers are used for malware detection. Furthermore, semantic control measures are applied to the existing crossover process to improve the generalization ability of Genetic Programming (GP).