Biblio

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.

The RFID technology has attracted considerable attention in recent years, and brings convenience to supply chain management. In this paper, we concentrate on designing path-checking protocols to check the valid paths in supply chains. By entering a valid path, the check reader can distinguish whether the tags have gone through the path or not. Based on modified schnorr signature scheme, we provide a path-checking method to achieve multi-signatures and final verification. In the end, we conduct security and privacy analysis to the scheme.