Survey on Cryptojacking Detection and Prevention in Cloud Systems

Abstract

Cryptojacking is one of the major threats that has come about in the area of cloud computing, and it refers to the secret mining of cryptocurrency through the use of an exploitation of computing resources. The said hacking method combines existing web and host methods, thus utilizing JavaScript, WASM, advertising, phishing, rootkits, and process obfuscation for avoiding detection. As such, AWS EC2, Azure VMs, Kubernetes, and Docker are some of the popular cloud platforms that are heavily targeted by this threat. Defending tactics currently employed heavily rely on behavioral scrutiny, anomaly detection, monitoring of network traffic, and also the employment of ML algorithms such as deep learning, reinforcement learning, and adversarial autoencoders. Unfortunately, issues with scalability, false alarm reduction, real-time monitoring, and threat response at an adaptive level persist among these solutions’ privacy-preserving learning over distributed data, multi-modal input data, as well as cloud-native SIEM & DevSecOps integration to support the security teams in being proactive rather than reactive. IAM, encryption and key management, and threat detection with Active Responder APIs are the indispensable building blocks for IaaS, PaaS, and SaaS model security. A smart, interpretable, as well as automated defense framework is the weapon of multi-cloud and serverless environments in winning the fight against cryptojacking, which keep on evolving.