Improved chimp optimization algorithm (ICOA) feature selection and deep neural network framework for internet of things (IOT) based android malware detection

Abstract

Internet of Things (IoT) is extensively implemented using Android applications thus detecting malicious Android apps is necessary. Malicious has been multiplying fast as a result of the growing usage of smartphones. The Android platform is often left vulnerable to new and undiscovered malware because to the increasing quantity and variety of Android malware that has considerably undermined the efficiency of the traditional defence systems. Several data-driven malware detection techniques were being out. Furthermore, these techniques face two major obstacles: How to acquire useful feature representations from raw data; How to lessen feature learning's reliance on past knowledge or individual workers. This research work proposed a system for detecting malware that begins by teaching rich characteristics an improved chimp optimization algorithm (ICOA) based feature selection and Deep Neural Network Framework (DNNF) for accurate detecting of Android malware. Deep learning methods' widespread use in the industry of feature representation learning served as an inspiration for this framework. Raw feature preprocessing, feature representation learning, and malware detection are the three key components of the proposed DNNF. The second phase is to learn high-level discriminative features to fuel malware identification using the ICOA. Finally, a DNNF based on Long Short-Term Memory (LSTM) is created for efficient malware detection in Android applications. Based on the findings of the simulations, it seems that the DNNF model that was suggested offers high recognition accuracy in comparison to other approaches that are considered to be state of the art.