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Boheung Chung

Publications and source records attributed to Boheung Chung.

2 recordsLinked to original sources

Classical Acceptance Is Not Hybrid Authentication: Validation Policy and Lifecycle Management of Hybrid X.509 Certificates in Deployed Open-Source Stacks

Post-quantum migration relies on hybrid X.509 certificates, which carry post-quantum material alongside the classical so existing verifiers still work. Several designs place it where a verifier may ignore it, so the classical path decides. We tested eight open-source path-validation stacks over seven independent codebases, one in two builds: nine configurations over six certificate profiles. On their default paths, every stack that parsed a separable hybrid certificate accepted it. Invalidating the post-quantum evidence in each separable scheme, leaving the classical evidence valid, changed no verdict in any of the 27 cells: none distinguished sound post-quantum evidence from destroyed. Four stacks verify post-quantum signatures elsewhere on the same path, so immature support does not explain it. Two stacks implement the checks the schemes specify, neither on its default path, and no document defines the interface between them: one carrying a relying party's hybrid requirement, an operational policy, into path validation and reporting which kind of acceptance resulted. We contribute a specification-derived model, this test, and a policy-parametric contract pairing a policy input with a labelled result. Revoking a bound post-quantum certificate changes no verdict in any of the nine configurations, because none consults it; the labelled result makes it visible to operations.

cs.CR

Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks

Connected and autonomous vehicles (CAVs) are an innovative form of traditional vehicles. Automotive Ethernet replaces the controller area network and FlexRay to support the large throughput required by high-definition applications. As CAVs have numerous functions, they exhibit a large attack surface and an increased vulnerability to attacks. However, no previous studies have focused on intrusion detection in automotive Ethernet-based networks. In this paper, we present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks in automotive Ethernet-based networks. To the best of our knowledge, this is the first such method developed for automotive Ethernet. The proposed intrusion detection model is based on feature generation and a convolutional neural network (CNN). To evaluate our intrusion detection system, we built a physical BroadR-Reach-based testbed and captured real AVTP packets. The experimental results show that the model exhibits outstanding performance: the F1-score and recall are greater than 0.9704 and 0.9949, respectively. In terms of the inference time per input and the generation intervals of AVTP traffic, our CNN model can readily be employed for real-time detection.

cs.CR