Following positive results, these companies have expressed strong interest in further experimentation and possible internal deployment.
Instead of simply performing binary classification on users (either malicious or benign this model identifies natural clusters of different user behaviors, and automatically extracts key features to interpret the captured behaviors.
The result produces clusters that capture users with similar behavioral patterns.
The core of our proposal is clickstream similarity graph, which uses similarity distance between pairs of clickstreams to capture user similarity.
Pages 19-26, seattle, Washington, USA August 06 - 11, 2006.Downloads (12 Months 387, downloads (6 Weeks 41).We demonstrate that our system achieves high detection accuracy with a minimal requirement of ground-truth inputs.In this paper, we develop a novel framework for user behavior modeling based on clickstream traces,.e., sequences of click events that users generate when using the online services.Article, bibliometrics, citation Count: 368, downloads (cumulative 6,366.ACM, new York, NY, USA 2006 table of contents, iSBN: doi.1145/1148170.On one hand, existing services cannot prevent attackers from creating large numbers of fake user accounts (or Sybils who generate massive amount of forged and malicious content such as fake online reviews, social spam, malware, and Sybil-based political lobbying efforts.Based on this clickstream model, we develop two practical systems: The first system is a semi-supervised system to detect malicious user accounts (Sybils).On the other hand, abusive behaviors from real users (e.g., cyberbullying, trolling) are significantly threatening the well being of online communities.Applying this system to Renren and another real-world online social network Whisper (100K users we help service providers to identify unexpected user behaviors (malicious accounts in Renren, hostile chatters in Whisper) and even predict users' future actions (dormant zynga poker hack 2012 final (no survey) zip users in Whisper).
We validate the system using ground-truth traces of 16,000 real and Sybil users from Renren, a large Chinese social network with 220M users.
Both systems have received positive feedback from our industrial collaborators including Renren, LinkedIn and Whisper, after testing our prototypes on their internal clickstream data.
The next generation of Internet services is driven by users and user generated content.
Full Text: PDF, get this Article, published in: Proceeding, sigir '06 Proceedings of the 29th annual international ACM sigir conference on Research and development in information retrieval.The second system is an unsupervised system to capture more fine-grained user behavior.User behaviors are diverse and often unpredictable, making it more challenging than ever to secure online services.Becoming increasingly common on user-generated comments found in Web forums.Used to describe the APIs (like categories, tags and technical features,.g.ACM Senior Member (2011).Seattle, Washington, USA August 06 - 11, 2006.We show that incorporating user behavior data can significantly improve ordering of top.