Identifying Malware on Cyber Physical Systems by incorporating Semi-Supervised Approach and Deep Learning

Abstract

Malicious applications can be a security threat to Cyber-physical systems as the Cyber-physical systems are composed of heterogeneous distributed systems and mostly depends on the internet, ICT services and products. The usage of ICT products and the services gives the opportunity of less expensive data collection, intelligent control and decision systems using automated data mining tools. Cyber-physical systems become exposed to the internet and the public networks as it has integrated to the ICT networks for easy automated options. Cyber-attacks can lead functional failure, blackouts, energy theft, data theft etc. and this will be critical security concern of Cyber-physical systems. At present, the mobile devices are replacing the pc environment and become a key element of Internet of Things. Therefore, it is essential to develop such a malware detection engine that will identify the mobile malware and reduce the spreading of the malicious code through mobile devices. This research work will identify the malware by incorporating semi-supervised approach and deep learning. The original and significant contributions are to propose an effective malware detection model by incorporating semi-supervised approach and deep learning, to implement the model using parallel processing and to evaluate the performance of the model using recent dataset. Here we have used the permission and the API call as the features. The proposed method has been tested on the real mobile malware data set and it shows improvement in accuracy. The Experimental results show that the deep learning along with semi-supervised method will be an effective way to identify the malware and it outperforms other detection methods.