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The profitability of fraud in online systems such as app markets and socialnetworks marks the failure of existing defense mechanisms. In this paper, wepropose FraudSys, a real-time fraud preemption approach that imposesBitcoin-inspired computational puzzles on the devices that post online systemactivities, such as reviews and likes. We introduce and leverage several novelconcepts that include (i) stateless, verifiable computational puzzles, thatimpose minimal performance overhead, but enable the efficient verification oftheir authenticity, (ii) a real-time, graph-based solution to assign fraudscores to user activities, and (iii) mechanisms to dynamically adjust puzzledifficulty levels based on fraud scores and the computational capabilities ofdevices. FraudSys does not alter the experience of users in online systems, butdelays fraudulent actions and consumes significant computational resources ofthe fraudsters. Using real datasets from Google Play and Facebook, wedemonstrate the feasibility of FraudSys by showing that the devices of honestusers are minimally impacted, while fraudster controlled devices receive dailycomputational penalties of up to 3,079 hours. In addition, we show that withFraudSys, fraud does not pay off, as a user equipped with mining hardware(e.g., AntMiner S7) will earn less than half through fraud than from honestBitcoin mining.

Stateless Puzzles for Real Time Online Fraud Preemption