MineShark: Cryptomining Traffic Detection at Scale

—The rapid growth of cryptojacking and the increase in regulatory bans on cryptomining have prompted organizations to enhance detection ability within their networks. Traditional methods, including rule-based detection and deep packet inspection, fall short in timely and comprehensively identifying new and encrypted mining threats. In contrast, learning-based techniques show promise by identifying content-agnostic traffic patterns, adapting to a wide range of cryptomining configurations. However, existing learning-based systems often lack scalability in real-world detection, primarily due to challenges with unlabeled, imbalanced, and high-speed traffic inputs. To address these issues, we introduce MineShark, a system that identifies robust patterns of mining traffic to distinguish between vast quantities of benign traffic and automates the confirmation of model outcomes through active probing to prevent an overload of model alarms. As model inference labels are progressively confirmed, MineShark conducts self-improving updates to enhance model accuracy. MineShark is capable of line-rate detection at various traffic volume scales with the allocation of different amounts of CPU and GPU resources. In a 10 Gbps campus network deployment lasting ten months, MineShark detected cryptomin-ing connections toward 105 mining pools ahead of concurrently deployed commercial systems, 17.6% of which were encrypted. It automatically filtered over 99.3% of false alarms and achieved an average packet processing throughput of 1.3 Mpps, meeting the line-rate demands of a 10 Gbps network, with a negligible loss rate of 0.2%. We publicize MineShark for broader use.