Hans D. Schotten

The paper proposes a robust semi-supervised temporal learning framework for cloud intrusion detection that explicitly handles adversarial contamination and temporal drift in unlabeled network traffic, significantly improving detection performance.

The paper proposes a resource-efficient, threshold-based authentication scheme for constrained IIoT devices using SRAM PUFs, addressing inherent unreliability through a combination of Hamming code error correction and Temporal Majority Voting.

The paper proposes a confidence-aware, multi-layered Cloud-IDS pipeline that integrates adaptive Q-Learning, Chroma memory, and LLM semantic analysis to enhance detection accuracy and reduce reliance on costly LLM escalations.