Comparative analysis of malicious android based software detection with recent metaheuristic algorithms

Today, Android malware threats and attacks are rapidly increasing due to their use and popularity. Therefore, the need for systems effectively detecting malware is also increasing day by day. This study proposes the use of various trending meta-heuristic algorithms with encapsulation method for optimal feature selection (FS) in Android malware detection. For this purpose, in this study, Artificial Bee Colony Algorithm (ABC), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Antlion Optimization (ALO), Crow Search Algorithm (CSA), Sine Cosine Algorithm (SCA), Whale Optimization. The ten most prominent current meta-heuristic algorithms (RMA) were used in feature selection, such as algorithm (WOA), Salp Swarm Algorithm (SSA), Harris Hawk Optimization (HHO) and Butterfly Optimization Algorithm (BOA). The efficiency of these algorithms is evaluated with five different machine learning (ML) methods on two well-known datasets of Android applications (Drebin-215 and MalGenome-215). In addition, the obtained results are based on five well-known traditional metaheuristic algorithms (CMA), which are widely used in solving this problem; It has also been compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Simulation Annealing (SA), Ant Colony Optimization (ACO) and Differential Evolution (DE). Extensive experimental results show that incorporating RMA into Android malware detection is a valuable approach.