Malware Detection Using Optimized Activation-Based Deep Belief Network: An Application on Internet of Things

Number of malware detection models has been proposed recently, which still poses major limitations in terms of detection rate. Hence, to overcome this, this paper introduces a new malware detection model with three stages: Feature Extraction, Feature selection and Classification. In feature extraction phase, the Term Frequency-Inverse Document Frequency (TF-IDF) and Information gain (IG) features are extracted. More importantly, the IG feature is subjected with the Holoentropy evaluation. Following the feature extraction phase feature selection is performed using Principle Component Analysis (PCA). Finally, to do the classification process, Deep Belief Network (DBN) is used with optimized activation function. To work out this optimization scenario, this paper intends to propose a new hybrid algorithm that combines the concept of Lion Algorithm (LA) and Glowworm Swarm Algorithm (GSO). The performance of proposed Lion Updated GSO (LU-GSO) is compared over other conventional models with respect to various evaluation measures and proves the betterments over others. Through the performance analysis, it was observed that the proposed model attains high accuracy, which is 10.21%, 10.04%, 9.18% and 6.42% better than LA, GSO, GWO and PSO, respectively.