Yield farming represents an immensely popular asset management activity in decentralized finance (DeFi). It involves supplying, borrowing, or staking crypto assets to earn an income in forms of transaction fees, interest, or participation rewards at different DeFi marketplaces. In this systematic survey, we present yield farming protocols as an aggregationlayer constituent of the wider DeFi ecosystem that interact with primitive-layer protocols such as decentralized exchanges (DEXs) and protocols for loanable funds (PLFs). We examine the yield farming mechanism by first studying the operations encoded in the yield farming smart contracts, and then performing stylized, parameterized simulations on various yield farming strategies. We conduct a thorough literature review on related work, and establish a framework for yield farming protocols that takes into account pool structure, accepted token types, and implemented strategies. Using our framework, we characterize major yield aggregators in the market including Yearn Finance, Beefy, and Badger DAO. Moreover, we discuss anecdotal attacks against yield aggregators and generalize a number of risks associated with yield farming.
In this paper, we propose a method that detects cryptojacking activities by analyzing content-agnostic network traffic flows. Our method first distinguishes crypto-mining activities by profiling the traffic with fast Fourier transform at each time window. It then generates the variation vectors between adjacent time windows and leverages a recurrent neural network to identify the cryptojacking patterns. Compared with the existing approaches, this method is privacy-preserving and can identify both browser-based and malware-based cryptojacking activities. Additionally, this method is easy to deploy. It can monitor all the devices within a network by accessing packet headers from the gateway router. CCS CONCEPTS • Networks → Network monitoring; • Security and privacy → Intrusion/anomaly detection and malware mitigation.
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