Title:

Comparative Study of Supervised Learning Methods for Malware Analysis, Journal of Telecommunications and Information Technology, 2014, nr 4

Creator:

Niewiadomska-Szynkiewicz, Ewa ; Kruczkowski, Michał

Subject and Keywords:

Support Vector Machine ; k-Nearest Neighbors ; malware analysis ; Naive Bayes ; data classification

Description:

Malware is a software designed to disrupt or even damage computer system or do other unwanted actions. Nowadays, malware is a common threat of the World Wide Web. Anti-malware protection and intrusion detection can be significantly supported by a comprehensive and extensive analysis of data on the Web. The aim of such analysis is a classification of the collected data into two sets, i.e., normal and malicious data. In this paper the authors investigate the use of three supervised learning methods for data mining to support the malware detection. The results of applications of Support Vector Machine, Naive Bayes and k-Nearest Neighbors techniques to classification of the data taken from devices located in many units, organizations and monitoring systems serviced by CERT Poland are described. The performance of all methods is compared and discussed. The results of performed experiments show that the supervised learning algorithms method can be successfully used to computer data analysis, and can support computer emergency response teams in threats detection.

Publisher:

Instytut Łączności - Państwowy Instytut Badawczy, Warszawa

Date:

2014, nr 4

Resource Type:

artykuł

Format:

application/pdf

Resource Identifier:

ISSN 1509-4553, on-line: ISSN 1899-8852

Source:

Journal of Telecommunications and Information Technology

Language:

ang

Rights Management:

Biblioteka Naukowa Instytutu Łączności