Lightweight Identification of Android Malware with Knowledge Distillation and Deep Learning Approach

Today, with the advancement of science and technology, the use of smartphones has become very common, and the Android operating system has been able to gain lots of popularity in the meantime. However, these devices face manysecurity challenges, including malware. Malware may cause many problems in both the security and privacy of users. So far, the state-of-the-art method in malware detection is based on deep learning, however, this approach requires a lot of computing resources and leads to high battery usage, which is unacceptable in smartphone devices. This paper proposes the knowledge distillation approach for lightening android malware detection. To this end, first, a heavy model is taught and then with the knowledge distillation approach, its knowledge is transferred to a light model called student. To simplify the learning process, soft labels are used here. The resulting model, although slightly less accurate in identification, has a much smaller size than the heavier model. Moreover, ensemble learning was proposed to recover the dropped accuracy. We have tested the proposed approach on CISC datasets including dynamic and static features, and the results show that the proposed method is not only able to lighten the model up to 99%, but also maintain the accuracy of the lightened model to the extent of the heavy model.