Abstract:

With the deep integration of the internet and emerging information technologies, multi-dimensional interconnectivity across industries, regions, and systems has become a core characteristic of modern technological development. The continuous growth and proliferation of blockchain cryptocurrencies have driven the large-scale expansion of illegal mining activities, posing persistent threats to personal privacy, corporate data assets, and critical information infrastructure. In this context, emergency response mechanisms against mining malware have been elevated to the national cybersecurity strategy level. This paper focused on the defense and remediation of mining malware attack chains by constructing a multi-dimensional monitoring system. To verify the feasibility of collaboration between multi-source detection and AI-based behavioral anomaly detection, the study integrated static, host, and network-level feature collection within an isolated environment. A stacking-based ensemble learning approach was adopted to unify multi-source scores and anomaly assessments for final decision-making, with periodic comparative evaluations conducted on detection performance and response latency. By leveraging multi-source detection techniques to reverse-model the propagation pathways of mining malware, a comprehensive emergency response framework was established, covering attack prevention, infection detection, and threat removal. The proposed collaborative defense mechanism combining multi-source detection and AI-driven behavioral analysis demonstrates superior detection effectiveness compared to traditional single-method detection techniques.

Key words: network security, network attacks, mining trojan, emergency response