Indexed by:
Abstract:
The recent growth in network usage has motivated the creation of new malicious code for various purposes, including economic ones. Today's signature-based anti-viruses are very accurate, but cannot detect new malicious code. Recently, classification algorithms were employed successfully for the detection of unknown malicious code. However, most of the studies use byte sequence n-gram representation of the binary code of the executable files on windows. We propose the use of Dalvik Operation Code on Android, generated by disassembling the application. We then use n-gram of the operation code as features for the classification process. We present a full methodology for the detection of unknown malicious code, based on text categorization concepts. The experiment results show that the method results are in a high accuracy rate.
Keyword:
Reprint Author's Address:
Email:
Source :
PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON AUTOMATIC CONTROL AND INFORMATION ENGINEERING (ICACIE 2017)
ISSN: 2352-5401
Year: 2017
Volume: 119
Page: 53-57
Language: English
Cited Count:
WoS CC Cited Count: 0
SCOPUS Cited Count:
ESI Highly Cited Papers on the List: 0 Unfold All
WanFang Cited Count:
Chinese Cited Count:
30 Days PV: 9
Affiliated Colleges: