Detecting and characterizing malicious websites

dc.contributor.advisorXu, Shouhuai
dc.contributor.authorXu, Li
dc.contributor.committeeMemberBylander, Tom
dc.contributor.committeeMemberMaynard, Hugh
dc.contributor.committeeMemberSandhu, Ravi
dc.contributor.committeeMemberXu, Maochao
dc.descriptionThe author has granted permission for their work to be available to the general public.
dc.description.abstractMalicious websites have become a big cyber threat. Given that malicious websites are inevitable, we need good solutions for detecting them. The present dissertation makes three contributions that are centered on addressing the malicious websites problem. First, it presents a novel cross-layer method for detecting malicious websites, which essentially exploits the network-layer "lens" to expose more information about malicious websites. Evaluation based on some real data shows that cross-layer detection is about 50 times faster than the dynamic approach, while achieving almost the same detection effectiveness (in terms of accuracy, false -negative rate, and false-positive rate). Second, it presents a novel proactive detection method to deal with adaptive attacks that can be exploited to evade the static detection approach. By formulating a novel security model, it characterizes when proactive detection can achieve significant success against adaptive attacks. Third, it presents statistical characteristics on the evolution of malicious websites. The characteristics offer deeper understanding about the threat of malicious websites.
dc.description.departmentComputer Science
dc.format.extent93 pages
dc.subjectCross-layer deteciton
dc.subjectdynamic analysis
dc.subjecthybrid analysis
dc.subjectMalicious URL
dc.subjectstatic analysis
dc.subject.classificationComputer science
dc.subject.lcshMalware (Computer software)
dc.subject.lcshWeb sites -- Security measures
dc.titleDetecting and characterizing malicious websites
dcterms.accessRightspq_OA Science of Texas at San Antonio of Philosophy


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