Implementing Behavior Analysis to Detect Cryptojacking using Machine Learning

Abstract

The rise of cryptojacking, a surreptitious form of crypto currency mining that exploits computing resources without user consent, poses a significant threat to cyber security. This abstract proposes a novel approach to counter this menace by leveraging machine learning techniques for behavior analysis. The implementation involves the deployment of sophisticated algorithms [5] to scrutinize patterns and anomalies in system behavior, aiming to identify indicators associated with cryptojacking activities. By training the machine learning model on historical data, it becomes adept at distinguishing normal system behavior from cryptojacking instances. The proposed solution enhances traditional cybersecurity measures by providing a proactive [12] and adaptive defense mechanism against evolving cryptojacking techniques. This research represents a crucial step in the ongoing efforts to safeguard digital ecosystems from the growing threat of unauthorized cryptocurrency mining activities.