Preventing Cryptojacking Malware Intrusion Using Multi-Layered Detection Methods

Abstract

Cryptojacking, the forbidden exploitation of computing resources solely for cryptocurrency mining, represents as a continually growing cybersecurity threat due to it’s real ability for operating covertly across all multiple systems with networks. Typical safeguards, such as host detection, often fail to provide thorough security. Isolated oversight systems also fail, notably in detailed settings where coded data and clouded mining methods are common. This research suggests a multilayered architecture to meet certain issues, integrating behavior analysis across the network level, machine learning on the endpoint level, and signature-based techniques along the perimeter level. Through careful integration of all these varied methods, the architecture gives powerful, layered protection able to find cryptojacking actions throughout multiple vectors. The methodology uses data sets, such as CICIDS 2017 and UNSW-NB15 and actual network traffic, to evaluate how the framework works. The results obtained clearly show the fundamental ability of the proposed solution to precisely recognize malicious mining activities, even in encrypted and mixed traffic scenarios, fully achieving high detection rates along with effectively minimizing false positives. This thorough approach not only ensures more in scalability and greater in adaptability, but also establishes a solid foundation for even further research in defending against cryptojacking and in many other emerging cyber threats. vi