Exploring Hidden Markov Models for Virus Analysis: A Semantic Approach

被引：35

作者：

Austin, Thomas H. ^{[1
]}

Filiol, Eric ^{[1
]}

Josse, Sebastien ^{[1
]}

Stamp, Mark ^{[1
]}

机构：

[1] Univ Calif Santa Cruz, Santa Cruz, CA 95064 USA

来源：

PROCEEDINGS OF THE 46TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES | 2013年

关键词：

D O I：

10.1109/HICSS.2013.217

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Recent work has presented hidden Markov models (HMMs) as a compelling option for virus identification. However, to date little research has been done to identify the meaning of these hidden states. In this paper, we examine HMMs for four different compilers, hand-written assembly code, three virus construction kits, and a metamorphic virus in order to note similarities and differences in the hidden states of the HMMs. Furthermore, we develop the dueling HMM Strategy, which leverages our knowledge about different compilers for more precise identification. We hope that this approach will allow for the development of better virus detection tools based on HMMs.

引用

页码：5039 / 5048

页数：10