Intelligent Malware Detection Using a Neural Network Ensemble Based on a Hybrid Search Mechanism

Abstract

Malware threats have become increasingly dynamic and complex, and, accordingly, artificial intelligence techniques have become the focal point for cybersecurity, as they are viewed as being more suited to tackling modern malware incidents. Specifically, neural networks, with their strong generalisation performance capability, are able to address a wide range of cyber threats. This article outlines the development and testing of a neural network ensemble approach to malware detection, based on a hybrid search mechanism. In this mechanism, the optimising of individual networks is done by an adaptive memetic algorithm with tabu search, which is also used to improve hidden neurons and weights of neural networks. The adaptive memetic algorithm combines global and local search optimisation techniques in order to overcome premature convergence and obtain an optimal search outcome. The results from the testing prove that the proposed method is strongly adaptive and efficient in its detection of a range of malware threats, and that it generates better results than other existing methods.

Immune-Based System to Enhance Malware Detection

“…The main flaws within this method are: (1) the use of only static features and, (2) the run-time needed is relatively important (approximately 15 hours). Authors in [11] proposed an approach that combines global search and local search heuristics, through a memetic evolutionary search process. The tabusearch algorithm is used as the local search technique, to improve the quality and fitness of solutions through scouring the neighbourhood of each solution for better individuals.…”