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Towards Detecting Anomalous User Behavior in Online Social Networks., , , , , , and . USENIX Security Symposium, page 223-238. USENIX Association, (2014)Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks., , , , , , and . IEEE Symposium on Security and Privacy, page 707-723. IEEE, (2019)Sieve: actionable insights from monitored metrics in distributed systems., , , , , , , and . Middleware, page 14-27. ACM, (2017)Sharing social content from home: a measurement-driven feasibility study., , , and . NOSSDAV, page 45-50. ACM, (2011)Exploring the design space of social network-based Sybil defenses., , , , , , and . COMSNETS, page 1-8. IEEE, (2012)Defending against large-scale crawls in online social networks., , , , , , and . CoNEXT, page 325-336. ACM, (2012)Complexity vs. performance: empirical analysis of machine learning as a service., , , , , and . IMC, page 384-397. ACM, (2017)A Markov Chain Model for TCP NewReno Over Optical Burst Switching Networks., , and . GLOBECOM, page 2215-2219. IEEE, (2007)Strength in Numbers: Robust Tamper Detection in Crowd Computations., , , , , , , and . COSN, page 113-124. ACM, (2015)Canal: scaling social network-based Sybil tolerance schemes., , , , and . EuroSys, page 309-322. ACM, (2012)