KISS: Knowledge Integration System Service for ML End Attacks Detection and Classification

Machine Learning (ML) systems are integrated with other parts of modern cyberinfrastructure (CI), which forms the novel ML with Integrated Network (MLIN) structure. Various security tools are already employed in CI to detect malicious attacks. However, their operation is limited to a certain CI part or MLIN component without considering their integration and the ML end performance as the major indicator. We design and implement Knowledge Integration System Service (KISS) that introduces a novel approach to detect and classify attacks into adversarial attacks against ML end system (A-Attacks), and against base MLIN infrastructure (B-Attacks). Unlike traditional security mechanisms, KISS examines how attacks on individual MLIN components impact the overall ML end system performance, and extracts and integrates knowledge from each MLIN CI component to better distinguish between the attack types. As our major contributions, we (1) investigate the effects of A- and B-Attacks on ML systems, actively used in industry. We (2) develop the KISS prototype and verify it in practice. We demonstrate how KISS can be integrated into already established MLIN CI and combined with the traditional security tools. Our experiments verify that KISS improves attack detection and their initial classification between A- and B-Attacks in MLIN operational setups.