N-gram-based detection of new malicious code

被引：0

作者：

Abou-Assaleh, T ^{[1
]}

Cercone, N ^{[1
]}

Keselj, V ^{[1
]}

Sweidan, R ^{[1
]}

机构：

[1] Dalhousie Univ, Fac Comp Sci, Privacy & Secur Lab, Halifax, NS B3H 3J5, Canada

来源：

PROCEEDINGS OF THE 28TH ANNUAL INTERNATIONAL COMPUTER SOFTWARE AND APPLICATION CONFERENCE, WORKSHOP AND FAST ABSTRACTS | 2004年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The current commercial anti-virus software detects a virus only after the virus has appeared and caused damage. Motivated by the standard signature-based technique for detecting viruses, and a recent successful text classification method, we explore the idea of automatically detecting new malicious code using the collected dataset of the benign and malicious code. We obtained accuracy of 100% in the training data, and 98% in 3-fold cross-validation.

引用

页码：41 / 42

页数：2

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