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A Machine Learning Approach for Identifying Cryptojacking Attacks

A Machine Learning Approach for Identifying Cryptojacking Attacks

Gunjan Karade1, Prof. Pooja Hardiya2

Research Scholar, Department of CSE, SDBCE, Indore (India)1

Asst. Professor, Department of CSE, SDBCT, Indore (India)2

ABSTRACT: With the rise of popularity of cryptocurrencies such as Bitcoin, Libra, Ripple, Ethereum etc, more attacks on crypto-currencies have been seen. Crypto-jacking is the unauthorized use of someone else’s computer to mine cryptocurrency. Hackers do this by either getting the victim to click on a malicious link in an email that loads crypto-mining code on the computer, or by infecting a website or online ad with JavaScript code that auto-executes once loaded in the victim’s browser. Given the lower-risk/lower effort nature of crypto-jacking, the number of such incidents in 2018 were nearly double of those of ransomware attacks. Apart from the crypto-jackers, web-crypto-mining library providers also enabled benign publishers to use this mechanism as an alternative monetization schema for web in the era of declined ad revenue. The proposed work aims at detecting crypto jacking based on Machine Learning based approaches.

Keywords: Data-mining, Darkweb, Crypto Mining Crypto currency: Bitcoin; Crypto-jacking; Machine Learning