Abstract
Mining cryptocurrency with an unauthorized and unlawful access to a victim’s computer’s processing power is called cryptojacking. With the rise of cryp- tocurrency in the markets people took advantage of this new piece of technol- ogy through mining it and earning from it. When the need for more perfor- mance and money emerged people came out with unlawful activities to mine cryptocurrency using other’s devices without their consent. These activities have led people and their devices vulnerable for the sake of greed. This is a dis- tributive approach which uses victim’s machine to mine cryptocurrency using all its resources i.e., CPU, GPU etc. This doesn’t need any software installa- tion but just to visit sites which are embedded with the malicious scripts that helps accessing user’s device. The analogy of this study is not limited to detect the presence of cryptojacking but also to enable users to detect the presence of any kind of suspicious activity is performed on the device. Through Genetic Search algorithm we extracted some of the key metrics used by computers for monitor its resources and further used these metrics to classify the presence of any unauthorized activity and achieved 100% accuracy for classifying these instances. The classification was done using several classification algorithms such as SVM (kernel – “linear”), SVM (kernel – “Radial Bias Function”), Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Multi-Layer Perceptron. The Genetic Search algorithm mentioned earlier is a machine learning iterative technique which is based on natural selection it selects individuals from the current population as parents and uses them to generate the next generation of offspring and all this through a method called CfsSubsetEval which returns a fitness score to the child based on their dependency on other attributes. For comparison we also used several methods which also serves the same objective
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