Title :
Neural network approach to Locating Cryptography in object code
Author :
Wright, Jason L. ; Manic, Milos
Author_Institution :
Idaho Nat. Lab., Idaho Falls, ID, USA
Abstract :
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.
Keywords :
cryptography; invasive software; neural nets; artificial neural networks; locating cryptography; malware analysis; Artificial neural networks; Computer science; Cryptography; Frequency; Hardware; Laboratories; Libraries; Neural networks; Reverse engineering; US Government; cryptography; neural networks;
Conference_Titel :
Emerging Technologies & Factory Automation, 2009. ETFA 2009. IEEE Conference on
Conference_Location :
Mallorca
Print_ISBN :
978-1-4244-2727-7
Electronic_ISBN :
1946-0759
DOI :
10.1109/ETFA.2009.5347226