Mining String Feature for Malicious Binary Detection Based on Normalized CNN

Most famous malware defense tools depend on a large number of detect rules, which are time consuming to develop and require lots of professional experience. Meanwhile, even commercial tools may show high false-negative for some new coming malware, whose patterns were not curved in the prepared rules. This paper proposed the Normalized CNN based Malicious binary Detection method on condition of String, Feature mining (NCMDSF) to address the above problems. Firstly, amount of string feature was extracted from thousands of windows binary applications. Secondly, a 3-layer normalized CNN model, with normalization layer other than down sampling layer, was fit to detect malware. Finally, the proposed method NCMDSF was evaluated to discover malware from more than 1,000 windows binary applications by K-fold cross validation. Experimental results showed that, NCMDSF was superior to some other learning-based methods, including classical CNN, LSTM, normalized LSTM, and won higher true positive rate on the condition of same false positive rate. Furthermore, it successfully avoids over-fitting that occurs in deep learning methods without using normalization.