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Faculty of Computing and Information Technology
Document Details
Document Type
:
Article In Journal
Document Title
:
Detecting Internet Worms Using Data Mining Techniques
ديدان الإنترنت باستخدام تقنيات التعدين الكشف عن البيانات
Subject
:
Data mining, malware detection
Document Language
:
Arabic
Abstract
:
Internet worms pose a serious threat to computer ecurity. Traditional approaches using signatures to detect worms pose little danger to the zero day attacks. The focus of malware research is shifting from using signature patterns to identifying the malicious behavior displayed by the malwares. This paper presents a novel idea of extracting variable length instruction sequences that can identify worms from clean programs using data mining techniques. The analysis is facilitated by the program control flow information contained in the instruction sequences. Based upon general statistics gathered from these instruction sequences we formulated the problem as a binary classification problem and built tree based classifiers including decision tree, bagging and random forest. Our approach showed 95.6% detection rate on novel worms whose data was not used in the model building process.
ISSN
:
1690-4524
Journal Name
:
Journal of Systemics, Cybernetics and Informatics
Volume
:
6
Issue Number
:
6
Publishing Year
:
1430 AH
2009 AD
Article Type
:
Article
Added Date
:
Wednesday, February 16, 2011
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
معظم صديقي
Siddiqui, Muazzam
Researcher
Doctorate
maasiddiqui@kau.edu.sa
Files
File Name
Type
Description
29003.docx
docx
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