000000292 001__ 292
000000292 005__ 20140602090807.0
000000292 037__ $$aLERSSE-PRESENTATION-2014-001
000000292 100__ $$aYazan Boshmaf
000000292 245__ $$aThwarting fake accounts by predicting their victims
000000292 260__ $$c2014-03-24
000000292 300__ $$amult. p
000000292 520__ $$aTraditional fake account detection systems employed by today's online social networks rely on either features extracted from user activities, or ranks computed from the underlying social graph. We herein present a system that integrates both approaches to deliver a more resilient defense mechanism that is both efficient and effective. We present a two-phase, iterative technique to achieve this integration. First, we leverage the insight that harmful fake accounts connect with other users (i.e., victims) before mounting subsequent attacks. We therefore train a classifier to predict these victims using features extracted from the activities of known, non-fake accounts. Second, we observe that actual victims are located at the borderline between two subgraphs, effectively separating harmful fake accounts from other accounts in the social graph. We take advantage of this observation by using the predicted victims as "deflection points" for a short random walk that starts from a known, non-fake account that is not a victim. By ranking accounts based on their landing probability, we guarantee that most of the fake accounts have a strictly lower rank than non-fake accounts. The results of our experiments show that our technique can help in reducing the number of victims while providing a more robust ranking for fake accounts detection.
000000292 6531_ $$aSocial network security
000000292 6531_ $$aOnline social networks
000000292 6531_ $$aFake accounts detection
000000292 6531_ $$aGraph theory
000000292 6531_ $$aMachine learning
000000292 700__ $$aDionysios Logothetis
000000292 700__ $$aGeorgos Siganos
000000292 700__ $$aMatei Ripeanu
000000292 700__ $$aKonstantin Beznosov
000000292 8560_ $$fboshmaf@ece.ubc.ca
000000292 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/292/files/AAAI_2014_Social_Hacking.pdf
000000292 8564_ $$uhttp://lersse-dl.ece.ubc.ca/record/292/files/AAAI_2014_Social_Hacking.pdf?subformat=pdfa$$xpdfa
000000292 909C4 $$pBoshmaf et al. Thwarting fake accounts by predicting their victims. Invited talk at AAAI 2014 Spring Symposia, Social Hacking and Cognitive Security on the Internet and New Media, Stanford, CA, March, 2014.
000000292 980__ $$aPRESENTATION