Xing Zheng

Pengfei Jing, Mengyun Tang, Xiaorong Shi et al.

Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities and limitations across various applications, including natural language processing and code generation. Existing benchmarks like MMLU, C-Eval, and HumanEval assess general LLM performance but lack focus on specific expert domains such as cybersecurity. Previous attempts to create cybersecurity datasets have faced limitations, including insufficient data volume and a reliance on multiple-choice questions (MCQs). To address these gaps, we propose SecBench, a multi-dimensional benchmarking dataset designed to evaluate LLMs in the cybersecurity domain. SecBench includes questions in various formats (MCQs and short-answer questions (SAQs)), at different capability levels (Knowledge Retention and Logical Reasoning), in multiple languages (Chinese and English), and across various sub-domains. The dataset was constructed by collecting high-quality data from open sources and organizing a Cybersecurity Question Design Contest, resulting in 44,823 MCQs and 3,087 SAQs. Particularly, we used the powerful while cost-effective LLMs to (1). label the data and (2). constructing a grading agent for automatic evaluation of SAQs. Benchmarking results on 16 SOTA LLMs demonstrate the usability of SecBench, which is arguably the largest and most comprehensive benchmark dataset for LLMs in cybersecurity. More information about SecBench can be found at our website, and the dataset can be accessed via the artifact link.

Zheng Xing, Junting Chen

Radio maps enable intelligent wireless applications by capturing the spatial distribution of channel characteristics. However, conventional construction methods demand extensive location-labeled data, which are costly and impractical in many real-world scenarios. This paper presents a blind radio map construction framework that infers user trajectories from indoor multiple-input multiple-output (MIMO)-Orthogonal Frequency-Division Multiplexing (OFDM) channel measurements without relying on location labels. It first proves that channel state information (CSI) under non-line-of-sight (NLOS) exhibits spatial continuity under a quasi-specular environmental model, allowing the derivation of a CSI-distance metric that is proportional to the corresponding physical distance. For rectilinear trajectories in Poisson-distributed access point (AP) deployments, it is shown that the Cramer-Rao Lower Bound (CRLB) of localization error vanishes asymptotically, even under poor angular resolution. Building on these theoretical results, a spatially regularized Bayesian inference framework is developed that jointly estimates channel features, distinguishes line-of-sight (LOS)/NLOS conditions and recovers user trajectories. Experiments on a ray-tracing dataset demonstrate an average localization error of 0.68 m and a beam map reconstruction error of 3.3%, validating the effectiveness of the proposed blind mapping method.