Neural network approach to Locating Cryptography in object code
Jason L. Wright, Milos Manic · 2009
Finding and identifying cryptography is a growing concern in the malware analysis community. In this paper, artificial neural networks are used to classify functional blocks from a disassembled program as being either cryptography related or not. The resulting system, referred to as NNLC (neural net for locating cryptography) is presented and results of applying this system to various libraries are described.