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Researcher profile

Sheng Wen

Sheng Wen contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Cryptography and SecurityComputer VisionSoftware EngineeringArtificial Intelligenceeess.SYInformation RetrievalMachine LearningProgramming LanguagesSystems and Control

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Published work

9 published item(s)

preprint2026arXiv

Context-Free Grammar Inference for Complex Programming Languages in Black Box Settings

Grammar inference for complex programming languages remains a significant challenge, as existing approaches fail to scale to real world datasets within practical time constraints. In our experiments, none of the state-of-the-art tools, including Arvada, Treevada and Kedavra were able to infer grammars for complex languages such as C, C++, and Java within 48 hours. Arvada and Treevada perform grammar inference directly on full-length input examples, which proves inefficient for large files commonly found in such languages. While Kedavra introduces data decomposition to create shorter examples for grammar inference, its lexical analysis still relies on the original inputs. Additionally, its strict no-overgeneralization constraint limits the construction of complex grammars. To overcome these limitations, we propose Crucio, which builds a decomposition forest to extract short examples for lexical and grammar inference via a distributional matrix. Experimental results show that Crucio is the only method capable of successfully inferring grammars for complex programming languages (where the number of nonterminals is up to 23x greater than in prior benchmarks) within reasonable time limits. On the prior simple benchmark, Crucio achieves an average recall improvement of 1.37x and 1.19x over Treevada and Kedavra, respectively, and improves F1 scores by 1.21x and 1.13x.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Sword: Style-Robust World Models as Simulators via Dynamic Latent Bootstrapping for VLA Policy Post-Training

The integration of Vision-Language-Action (VLA) models with World Models has gained increasing attention. One representative approach treats learned World Models as generative simulators, enabling policy optimization entirely within "imagination." However, when deployed as simulators for specific environments such as the LIBERO benchmark, existing World Models often suffer from poor generalization and long-horizon error accumulation. During closed-loop rollouts, these models are highly sensitive to initial-state perturbations; minor changes in color, illumination, and other visual factors can trigger cascading hallucinations, leading to severe blurriness or overexposure. Moreover, long-horizon error accumulation further degrades the quality and fidelity of predicted future states. These issues limit the reliability of World Models as simulators. To mitigate these problems, we propose Sword, a robust World Model framework. Our method introduces Structure-Guided Style Augmentation to disentangle the visual textures of interactive environments from task-relevant dynamics, thereby improving generalization. We further propose Dynamic Latent Bootstrapping, which maintains consistency between training and inference while keeping memory consumption low. Extensive experiments on the LIBERO benchmark show that our method significantly outperforms the baseline WoVR in terms of generalization, generation quality, robustness, fidelity, and the success rate of reinforcement-learning post-training for VLA models.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Characterizing Sensor Leaks in Android Apps

While extremely valuable to achieve advanced functions, mobile phone sensors can be abused by attackers to implement malicious activities in Android apps, as experimentally demonstrated by many state-of-the-art studies. There is hence a strong need to regulate the usage of mobile sensors so as to keep them from being exploited by malicious attackers. However, despite the fact that various efforts have been put in achieving this, i.e., detecting privacy leaks in Android apps, we have not yet found approaches to automatically detect sensor leaks in Android apps. To fill the gap, we designed and implemented a novel prototype tool, SEEKER, that extends the famous FlowDroid tool to detect sensor-based data leaks in Android apps. SEEKER conducts sensor-focused static taint analyses directly on the Android apps' bytecode and reports not only sensor-triggered privacy leaks but also the sensor types involved in the leaks. Experimental results using over 40,000 real-world Android apps show that SEEKER is effective in detecting sensor leaks in Android apps, and malicious apps are more interested in leaking sensor data than benign apps.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Path Transitions Tell More:Optimizing Fuzzing Schedules via Runtime Program States

Coverage-guided Greybox Fuzzing (CGF) is one of the most successful and widely-used techniques for bug hunting. Two major approaches are adopted to optimize CGF: (i) to reduce search space of inputs by inferring relationships between input bytes and path constraints; (ii) to formulate fuzzing processes (e.g., path transitions) and build up probability distributions to optimize power schedules, i.e., the number of inputs generated per seed. However, the former is subjective to the inference results which may include extra bytes for a path constraint, thereby limiting the efficiency of path constraints resolution, code coverage discovery, and bugs exposure; the latter formalization, concentrating on power schedules for seeds alone, is inattentive to the schedule for bytes in a seed. In this paper, we propose a lightweight fuzzing approach, Truzz, to optimize existing Coverage-guided Greybox Fuzzers (CGFs). To address two aforementioned challenges, Truzz identifies the bytes related to the validation checks (i.e., the checks guarding error-handling code), and protects those bytes from being frequently mutated, making most generated inputs examine the functionalities of programs, in lieu of being rejected by validation checks. The byte-wise relationship determination mitigates the problem of loading extra bytes when fuzzers infer the byte-constraint relation. Furthermore, the proposed path transition within Truzz can efficiently prioritize the seed as the new path, harvesting many new edges, and the new path likely belongs to a code region with many undiscovered code lines. The experimental results show that on average, Truzz can generate 16.14% more inputs flowing into functional code, in addition to 24.75% more new edges than the vanilla fuzzers. Finally, our approach exposes 13 bugs in 8 target programs, and 6 of them have not been identified by the vanilla fuzzers.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by persisting the security knowledge gained from different cybersecurity context. Prior studies neither systematically analysed the wide-range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Although LDA has been widely adopted in topic generation, its generated topics cannot cover the cybersecurity concepts completely and considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories instead of LDA-generated topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings. (1) The impact reflected from cyber-security texts strongly correlates with the monetary loss caused by cybercrimes. (2) For most categories, security blogs share the largest popularity and largest absolute/relative impact over time. (3) Websites deliver security information without caring about timeliness much, where one third of the articles do not specify the date and the rest have a time lag in posting emerging security issues.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Catering to Your Concerns: Automatic Generation of Personalised Security-Centric Descriptions for Android Apps

Android users are increasingly concerned with the privacy of their data and security of their devices. To improve the security awareness of users, recent automatic techniques produce security-centric descriptions by performing program analysis. However, the generated text does not always address users' concerns as they are generally too technical to be understood by ordinary users. Moreover, different users have varied linguistic preferences, which do not match the text. Motivated by this challenge, we develop an innovative scheme to help users avoid malware and privacy-breaching apps by generating security descriptions that explain the privacy and security related aspects of an Android app in clear and understandable terms. We implement a prototype system, PERSCRIPTION, to generate personalised security-centric descriptions that automatically learn users' security concerns and linguistic preferences to produce user-oriented descriptions. We evaluate our scheme through experiments and user studies. The results clearly demonstrate the improvement on readability and users' security awareness of PERSCRIPTION's descriptions compared to existing description generators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Daedalus: Breaking Non-Maximum Suppression in Object Detection via Adversarial Examples

This paper demonstrates that Non-Maximum Suppression (NMS), which is commonly used in Object Detection (OD) tasks to filter redundant detection results, is no longer secure. Considering that NMS has been an integral part of OD systems, thwarting the functionality of NMS can result in unexpected or even lethal consequences for such systems. In this paper, an adversarial example attack which triggers malfunctioning of NMS in end-to-end OD models is proposed. The attack, namely \texttt{Daedalus}, compresses the dimensions of detection boxes to evade NMS. As a result, the final detection output contains extremely dense false positives. This can be fatal for many OD applications such as autonomous vehicles and surveillance systems. The attack can be generalised to different end-to-end OD models, such that the attack cripples various OD applications. Furthermore, a way to craft robust adversarial examples is developed by using an ensemble of popular detection models as the substitutes. Considering the pervasive nature of model reusing in real-world OD scenarios, Daedalus examples crafted based on an \textit{ensemble of substitutes} can launch attacks without knowing the parameters of the victim models. Experimental results demonstrate that the attack effectively stops NMS from filtering redundant bounding boxes. As the evaluation results suggest, Daedalus increases the false positive rate in detection results to $99.9\%$ and reduces the mean average precision scores to $0$, while maintaining a low cost of distortion on the original inputs. It is also demonstrated that the attack can be practically launched against real-world OD systems via printed posters.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Defending against Adversarial Attack towards Deep Neural Networks via Collaborative Multi-task Training

Deep neural networks (DNNs) are known to be vulnerable to adversarial examples which contain human-imperceptible perturbations. A series of defending methods, either proactive defence or reactive defence, have been proposed in the recent years. However, most of the methods can only handle specific attacks. For example, proactive defending methods are invalid against grey-box or white-box attacks, while reactive defending methods are challenged by low-distortion adversarial examples or transferring adversarial examples. This becomes a critical problem since a defender usually does not have the type of the attack as a priori knowledge. Moreover, existing two-pronged defences (e.g., MagNet), which take advantages of both proactive and reactive methods, have been reported as broken under transferring attacks. To address this problem, this paper proposed a novel defensive framework based on collaborative multi-task training, aiming at providing defence for different types of attacks. The proposed defence first encodes training labels into label pairs and counters black-box attacks leveraging adversarial training supervised by the encoded label pairs. The defence further constructs a detector to identify and reject high-confidence adversarial examples that bypass the black-box defence. In addition, the proposed collaborative architecture can prevent adversaries from finding valid adversarial examples when the defence strategy is exposed. In the experiments, we evaluated our defence against four state-of-the-art attacks on $MNIST$ and $CIFAR10$ datasets. The results showed that our defending method achieved up to $96.3\%$ classification accuracy on black-box adversarial examples, and detected up to $98.7\%$ of the high confidence adversarial examples. It only decreased the model accuracy on benign example classification by $2.1\%$ for the $CIFAR10$ dataset.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Security of Networked Control Systems in Smart Vehicle and its Adaptive Cruise Control

With the benefits of Internet of Vehicles (IoV) paradigm, come along unprecedented security challenges. Among many applications of inter-connected systems, vehicular networks and smart cars are examples that are already rolled out. Smart vehicles not only have networks connecting their internal components e.g. via Controller Area Network (CAN) bus, but also are connected to the outside world through road side units and other vehicles. In some cases, the internal and external network packets pass through the same hardware and are merely isolated by software defined rules. Any misconfiguration opens a window for the hackers to intrude into vehicles' internal components e.g. central lock system, Engine Control Unit (ECU), Anti-lock Braking System (ABS) or Adaptive Cruise Control (ACC) system. Compromise of any of these can lead to disastrous outcomes. In this paper, we study the security of smart vehicles' adaptive cruise control systems in the presence of covert attacks. We define two covert/stealth attacks in the context of cruise control and propose a novel intrusion detection and compensation method to disclose and respond to such attacks. More precisely, we focus on the covert cyber attacks that compromise the integrity of cruise controller and employ a neural network identifier in the IDS engine to estimate the system output dynamically and compare it against the ACC output. If any anomaly is detected, an embedded substitute controller kicks in and takes over the control. We conducted extensive experiments in MATLAB to evaluate the effectiveness of the proposed scheme in a simulated environment.

0 citations0 reviewsNo code linkedOpen access