CoinPolice:Detecting Hidden Cryptojacking Attacks with Neural Networks
This addresses the issue of cryptojacking for web users and security practitioners by providing a robust detection method against all known evasion techniques, though it is incremental as it builds on prior defenses.
The paper tackles the problem of detecting hidden cryptojacking attacks that evade existing defenses by using a novel method, CoinPolice, which artificially varies CPU power to observe throttling and employs a deep neural network classifier, achieving 97.87% detection with a 0.74% false positive rate and identifying 6700 sites in a large-scale investigation.
Traffic monetization is a crucial component of running most for-profit online businesses. One of its latest incarnations is cryptocurrency mining, where a website instructs the visitor's browser to participate in building a cryptocurrency ledger (e.g., Bitcoin, Monero) in exchange for a small reward in the same currency. In its essence, this practice trades the user's electric bill (or battery level) for cryptocurrency. With user consent, this exchange can be a legitimate funding source - for example, UNICEF has collected over 27k charity donations on a website dedicated to this purpose, thehopepage.org. Regrettably, this practice also easily lends itself to abuse: in this form, called cryptojacking, attacks surreptitiously mine in the users browser, and profits are collected either by website owners or by hackers that planted the mining script into a vulnerable page. Cryptojackers have been bettering their evasion techniques, incorporating in their toolkits domain fluxing, content obfuscation, the use of WebAssembly, and throttling. Whereas most state-of-the-art defenses address multiple of these evasion techniques, none is resistant against all. In this paper, we offer a novel detection method, CoinPolice, that is robust against all of the aforementioned evasion techniques. CoinPolice flips throttling against cryptojackers, artificially varying the browser's CPU power to observe the presence of throttling. Based on a deep neural network classifier, CoinPolice can detect 97.87% of hidden miners with a low false positive rate (0.74%). We compare CoinPolice performance with the current state of the art and show our approach outperforms it when detecting aggressively throttled miners. Finally, we deploy Coinpolice to perform the largest-scale cryptoming investigation to date, identifying 6700 sites that monetize traffic in this fashion.