Mitigating vanishing similarity in quantum kernels for DDoS attack detection

Abstract Distributed Denial-of-Service (DDoS) attacks remain one of the most critical threats to modern cybersecurity. While machine learning techniques have proven effective for detection, classical approaches struggle with the growing complexity and scale of these attacks. Quantum computing, particularly quantum kernel methods, offers a promising alternative; however, the current state of the art faces a major challenge: vanishing similarity, which severely limits model expressiveness in high-dimensional spaces. This work introduces a novel quantum kernel inspired by multiple kernel learning, designed to mitigate vanishing similarity by constructing kernels in reduced-dimensional subspaces and combining them through averaging. The methodology is validated on the Canadian Institute for Cybersecurity dataset (NTP-based DDoS attacks). The proposed kernel effectively preserves classification capability in high-dimensional feature spaces, paving the way for practical applications of quantum kernels.