A meta data mining framework for botnet analysis. Issue 5 (3rd September 2019)

• Record Type: Journal Article
• Authors: Haque, Afzalul; Ayyar, Amrit Venkat; Singh, Sanjay
• Is Part Of: International journal of computers and applications. Volume 41:Issue 5(2019)
• Page Start: 392
• Page End: 399
• Publication Date: 2019-09-03
• Subjects: Botnet detection -- botnet meta-data mining -- optimized apriori algorithm; Computers -- Periodicals; Computer software -- Periodicals; Computer networks -- Periodicals; Multimedia systems -- Periodicals; Internet -- Periodicals; World Wide Web -- Periodicals; Minicomputers -- Periodicals; Microcomputers -- Periodicals; 004.05
• DOI: 10.1080/1206212X.2018.1442136
• Languages: English
• ISSNs: 1206-212X

Abstract

ABSTRACT: Botnets are a group of compromised computers that act in a coordinated manner against a target determined by a single point of control. Meta-analysis of botnets is crucial as it results in knowledge about the botnet, often providing valuable information to researchers who are looking to eradicate it. However, meta-analysis has not been applied from a research standpoint for botnets detection and analysis. This paper proposes a framework that uses modified implementation of Apriori data mining algorithms on data-sets derived from end-user logs for meta-analysis. It also presents a case study following the proposed approach. The results of this case study present some interesting heuristics that can be used to eradicate the botnet. These heuristics include the indication of vulnerabilities, new trends in botnet malware among others.