arXiv subjects

Jianwei Zhuge

Publications and source records attributed to Jianwei Zhuge.

3 recordsLinked to original sources

arXiv2026

TRACE: Timely Retrieval and Alignment for Cybersecurity Knowledge Graph Construction and Expansion

The rapid evolution of cyber threats has highlighted significant gaps in security knowledge integration. Cybersecurity Knowledge Graphs (CKGs) relying on structured data inherently exhibit hysteresis, as the timely incorporation of rapidly evolving unstructured data remains limited, potentially leading to the omission of critical insights for risk analysis. To address these limitations, we introduce TRACE, a framework designed to integrate structured and unstructured cybersecurity data sources. TRACE integrates knowledge from 24 structured databases and 3 categories of unstructured data, including APT reports, papers, and repair notices. Leveraging Large Language Models (LLMs), TRACE facilitates efficient entity extraction and alignment, enabling continuous updates to the CKG. Evaluations demonstrate that TRACE achieves a 1.8x increase in node coverage compared to existing CKGs. TRACE attains the precision of 86.08%, the recall of 76.92%, and the F1 score of 81.24% in entity extraction, surpassing the best-known LLM-based baselines by 7.8%. Furthermore, our entity alignment methods effectively harmonize entities with existing knowledge structures, enhancing the integrity and utility of the CKG. With TRACE, threat hunters and attack analysts gain real-time, holistic insights into vulnerabilities, attack methods, and defense technologies.

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arXiv2025

Physical-Layer Signal Injection Attacks on EV Charging Ports: Bypassing Authentication via Electrical-Level Exploits

The proliferation of electric vehicles in recent years has significantly expanded the charging infrastructure while introducing new security risks to both vehicles and chargers. In this paper, we investigate the security of major charging protocols such as SAE J1772, CCS, IEC 61851, GB/T 20234, and NACS, uncovering new physical signal spoofing attacks in their authentication mechanisms. By inserting a compact malicious device into the charger connector, attackers can inject fraudulent signals to sabotage the charging process, leading to denial of service, vehicle-induced charger lockout, and damage to the chargers or the vehicle's charge management system. To demonstrate the feasibility of our attacks, we propose PORTulator, a proof-of-concept (PoC) attack hardware, including a charger gun plugin device for injecting physical signals and a wireless controller for remote manipulation. By evaluating PORTulator on multiple real-world chargers, we identify 7 charging standards used by 20 charger piles that are vulnerable to our attacks. The root cause is that chargers use simple physical signals for authentication and control, making them easily spoofed by attackers. To address this issue, we propose enhancing authentication circuits by integrating non-resistive memory components and utilizing dynamic high-frequency Pulse Width Modulation (PWM) signals to counter such physical signal spoofing attacks.

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arXiv2022

jTrans: Jump-Aware Transformer for Binary Code Similarity

Binary code similarity detection (BCSD) has important applications in various fields such as vulnerability detection, software component analysis, and reverse engineering. Recent studies have shown that deep neural networks (DNNs) can comprehend instructions or control-flow graphs (CFG) of binary code and support BCSD. In this study, we propose a novel Transformer-based approach, namely jTrans, to learn representations of binary code. It is the first solution that embeds control flow information of binary code into Transformer-based language models, by using a novel jump-aware representation of the analyzed binaries and a newly-designed pre-training task. Additionally, we release to the community a newly-created large dataset of binaries, BinaryCorp, which is the most diverse to date. Evaluation results show that jTrans outperforms state-of-the-art (SOTA) approaches on this more challenging dataset by 30.5% (i.e., from 32.0% to 62.5%). In a real-world task of known vulnerability searching, jTrans achieves a recall that is 2X higher than existing SOTA baselines.

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