Risul Islam, Ben Treves, Md Omar Faruk Rokon, M. Faloutsos
{"title":"LinkMan: hyperlink-driven misbehavior detection in online security forums","authors":"Risul Islam, Ben Treves, Md Omar Faruk Rokon, M. Faloutsos","doi":"10.1145/3487351.3488323","DOIUrl":null,"url":null,"abstract":"How can we detect and analyze hyperlink-driven misbehavior in online forums? Online forums contain enormous amounts of user-generated content, with threads and comments frequently supplemented by hyperlinks. These hyperlinks are often posted with malicious intention and we refer to this as 'hyperlink-driven misbehavior'. We present LinkMan, a systematic suite of capabilities, to detect and analyze hyperlink-driven misbehavior in online forums. We take a unique perspective focusing on hyperlink sharing practices of the users to spot misbehavior. LinkMan can categorize these hyperlinks as: a) phishing, b) spamming, and b) promoting malicious products. Our approach consists of three high-level phases: (a) extracting hyperlinks from the textual data, (b) identifying misbehaving hyperlinks, and (c) modeling the behavioral patterns of hyperlink sharing, where we identify key hyperlinks and analyze the collaboration dynamics of hyperlink sharing. In addition, we implement our approach as a powerful and easy-to-use open platform for practitioners. We apply LinkMan to spot misbehavior from three online security forums, where we expect the users to be more security-aware. We show that our approach works very well in terms of retrieving and classifying hyperlinks compared to previous solutions. Furthermore, we find non-trivial and often systematic misbehavior: (a) we find a total of 637 misbehaving hyperlinks, and (b) we identify 30 colluding groups of users in terms of promoting hyperlinks. Our work is a significant step towards mining online forums and detecting misbehaving users comprehensively.","PeriodicalId":320904,"journal":{"name":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3487351.3488323","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
How can we detect and analyze hyperlink-driven misbehavior in online forums? Online forums contain enormous amounts of user-generated content, with threads and comments frequently supplemented by hyperlinks. These hyperlinks are often posted with malicious intention and we refer to this as 'hyperlink-driven misbehavior'. We present LinkMan, a systematic suite of capabilities, to detect and analyze hyperlink-driven misbehavior in online forums. We take a unique perspective focusing on hyperlink sharing practices of the users to spot misbehavior. LinkMan can categorize these hyperlinks as: a) phishing, b) spamming, and b) promoting malicious products. Our approach consists of three high-level phases: (a) extracting hyperlinks from the textual data, (b) identifying misbehaving hyperlinks, and (c) modeling the behavioral patterns of hyperlink sharing, where we identify key hyperlinks and analyze the collaboration dynamics of hyperlink sharing. In addition, we implement our approach as a powerful and easy-to-use open platform for practitioners. We apply LinkMan to spot misbehavior from three online security forums, where we expect the users to be more security-aware. We show that our approach works very well in terms of retrieving and classifying hyperlinks compared to previous solutions. Furthermore, we find non-trivial and often systematic misbehavior: (a) we find a total of 637 misbehaving hyperlinks, and (b) we identify 30 colluding groups of users in terms of promoting hyperlinks. Our work is a significant step towards mining online forums and detecting misbehaving users comprehensively.