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mVulPreter: A Multi-Granularity Vulnerability Detection System With Interpretations

IEEE Transactions on Dependable and Secure Computing 2022年 1-12页

作者： Zou, Deqing Hu, Yutao Li, Wenke Wu, Yueming Zhao, Haojun Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to the powerful automatic feature extraction, deep learning-based vulnerability detection methods have evolved significantly in recent years. However, almost all current work focuses on detecting vulnerabilities at a single granularity (i.e., slice-level or function-level). In practice, slice-level vulnerability detection is fine-grained but may contain incomplete vulnerability details. Function-level vulnerability detection includes full vulnerability semantics but may contain vulnerability-unrelated statements. Meanwhile, they pay more attention to predicting whether the source code is vulnerable and cannot pinpoint which statements are more likely to be vulnerable. In this paper, we design mVulPreter, a multi-granularity vulnerability detector that can provide interpretations of detection results. Specifically, we propose a novel technique to effectively blend the advantages of function-level and slice-level vulnerability detection models and output the detection results' interpretation only by the model itself. We evaluate mVulPreter on a dataset containing 5,310 vulnerable functions and 7,601 non-vulnerable functions. The experimental results indicate that mVulPreter outperforms existing state-of-the-art vulnerability detection approaches (i.e., Checkmarx, FlawFinder, RATS, TokenCNN, StatementLSTM, SySeVR, and Devign). IEEE

关键词： Semantics

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Traceable Ring Signature Scheme in Internet of Vehicles Based on Lattice Cryptography 21

Traceable Ring Signature Scheme in Internet of Vehicles Base...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on big data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liao, Wei Han, Lansheng Chen, Peng Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China

ISBN: (纸本)9798350329223

Lattice cryptography, as a recognized Cryptosystem that can resist quantum computation, has great potential for development. Lattice based signature scheme is currently a research focus. In this paper, the traceable ring signature scheme based on lattice cryptography in the Internet of Vehicles (IoV) environment is studied. Firstly, users' public and private keys are generated on the lattice, which effectively reduces the key size and time cost. Secondly, the scheme also achieves traceability by adding additional information. The scheme satisfies strong unforgeability, traceability, and anonymity. In addition, the traceable ring signature scheme based on lattice cryptography is applied in the IoV environment to effectively protect user privacy. Model communication can not only protect user privacy, but also trace the signer when necessary, achieving a balance between security and privacy. The simulation results show that the average delay of IoV of our scheme is better than that of similar schemes. The research in this paper improves the security of data transmission in the IoV environment, enables it to adapt to the complex and changeable new network environment, and effectively guarantees the information security of vehicles and owners. © 2023 IEEE.

关键词： Cryptography

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Reveal training performance mystery between Tensor Flow and PyTorch in the single GPU environment

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science China(Information sciences) 2022年第1期65卷 147-163页

作者： Hulin DAI Xuan PENG Xuanhua SHI Ligang HE Qian XIONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabSchool of Computer Science and Technology Huazhong University of Science and Technology Department of Computer Science University of Warwick

Deep learning has gained tremendous success in various fields while training deep neural networks(DNNs) is very compute-intensive, which results in numerous deep learning frameworks that aim to offer better usability and higher performance to deep learning practitioners. Tensor Flow and Py Torch are the two most popular frameworks. Tensor Flow is more promising within the industry context, while Py Torch is more appealing in academia. However, these two frameworks differ much owing to the opposite design philosophy:static vs dynamic computation graph. Tensor Flow is regarded as being more performance-friendly as it has more opportunities to perform optimizations with the full view of the computation graph. However, there are also claims that Py Torch is faster than Tensor Flow sometimes, which confuses the end-users on the choice between them. In this paper, we carry out the analytical and experimental analysis to unravel the mystery of comparison in training speed on single-GPU between Tensor Flow and Py Torch. To ensure that our investigation is as comprehensive as possible, we carefully select seven popular neural networks, which cover computer vision, speech recognition, and natural language processing(NLP). The contributions of this work are two-fold. First, we conduct the detailed benchmarking experiments on Tensor Flow and Py Torch and analyze the reasons for their performance difference. This work provides the guidance for the end-users to choose between these two frameworks. Second, we identify some key factors that affect the performance,which can direct the end-users to write their models more efficiently.

关键词： deep learning performance comparison TensorFlow PyTorch

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Backdoor Attacks on Bimodal Salient Object Detection with RGB-Thermal data 24

Backdoor Attacks on Bimodal Salient Object Detection with RG...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yin, Wen Zhu, Bin Benjamin Xie, Yulai Zhou, Pan Feng, Dan School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Microsoft Corporation Beijing China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400706868

RGB-Thermal Salient Object Detection (RGBT-SOD) plays a critical role in complex scene recognition applications, such as autonomous driving. However, security research in this domain is still in its infancy. This paper presents the first backdoor attack on RGBT-SOD systems, generating saliency maps on triggered inputs that depict non-existent salient objects chosen by the attacker or falsely mark an entire image as fully salient or entirely non-salient. We uncover that triggers have an influence range for generating non-existent salient objects, supported by a theoretical analysis. Extensive experiments show the effectiveness of our attack in both digital and physical-world scenarios. Notably, our dual-modality backdoor attack achieves an Attack Success Rate (ASR) of 86.72% with only five pairs of poisoned images in model training. After investigating potential countermeasures, we find them inadequate in mitigating our attacks, highlighting the urgent need for robust defenses against sophisticated backdoor attacks in RGBT-SOD systems. © 2024 ACM.

关键词： Object detection

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Towards trustworthy blockchain systems in the era of “Internet of value”: development, challenges, and future trends

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science China(Information sciences) 2022年第5期65卷 250-260页

作者： Hai JIN Jiang XIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology

Since the advent of cryptocurrencies such as Bitcoin, blockchain, as their underlying technologies,has drawn a massive amount of attention from both academia and the industry. This ever-evolving technology inherits the “genes” of distributed systems, offering significant advantages of immutability, transparency,auditability, and tamper-resistance. These benefits help blockchain re-establish public confidence, and hold the significant promise of reliable information sharing and value transfer. Therefore, blockchain has become the foundation of crucial strategic deployments in countries across the world, and the fundamental basis for building the next generation Web 3.0 — “Internet of value”. In this article, we will start with unraveling the essential ingredients of blockchain technology, and showing the characteristics of each of these ingredients in the context of distributed systems. We will then present the core technical challenges that need to be addressed prior to unleashing its full potential, including its performance, scalability, and cross-chain interoperability. Finally, we will introduce the recent developments of blockchain systems, and discuss the future trends of the blockchain ecosystem.

关键词： blockchain distributed systems internet of value challenges development

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Massively parallel algorithms for fully dynamic all-pairs shortest paths

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Frontiers of Computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Soft-GNN:towards robust graph neural networks via self-adaptive data utilization

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Frontiers of Computer science 2025年第4期19卷 1-12页

作者： Yao WU Hong HUANG Yu SONG Hai JIN National Engineering Research Center for Big Data Technology and System Service Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China College of Information and Communication National University of Defense TechnologyWuhan 430019China Department of Computer Science and Operations Research Universitéde MontréalMontreal H3C 3J7Canada

Graph neural networks(GNNs)have gained traction and have been applied to various graph-based data analysis tasks due to their high ***,a major concern is their robustness,particularly when faced with graph data that has been deliberately or accidentally polluted with *** presents a challenge in learning robust GNNs under noisy *** address this issue,we propose a novel framework called Soft-GNN,which mitigates the influence of label noise by adapting the data utilized in *** approach employs a dynamic data utilization strategy that estimates adaptive weights based on prediction deviation,local deviation,and global *** better utilizing significant training samples and reducing the impact of label noise through dynamic data selection,GNNs are trained to be more *** evaluate the performance,robustness,generality,and complexity of our model on five real-world datasets,and our experimental results demonstrate the superiority of our approach over existing methods.

关键词： graph neural networks node classification label noise robustness

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ANIMC: A Soft Approach for Autoweighted Noisy and Incomplete Multiview Clustering

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2022年第2期3卷 192-206页

作者： Fang, Xiang Hu, Yuchong Zhou, Pan Wu, Dapeng Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering The School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Information Storage System Ministry of Education of China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Electrical and Computer Engineering University of Florida GainesvilleFL32611 United States

Multiview clustering has wide real-world applications because it can process data from multiple sources. However, these data often contain missing instances and noises, which are ignored by most multiview clustering methods. Missing instances may make these methods difficult to use directly, and noises will lead to unreliable clustering results. In this article, we propose a novel autoweighted noisy and incomplete multiview clustering (ANIMC) approach via a soft autoweighted strategy and a doubly soft regular regression model. First, by designing adaptive semiregularized nonnegative matrix factorization, the soft autoweighted strategy assigns a proper weight to each view and adds a soft boundary to balance the influence of noises and incompleteness. Second, by proposing θ-norm, the doubly soft regularized regression model adjusts the sparsity of our model by choosing different θ. Compared with previous methods, ANIMC has three unique advantages: 1) it is a soft algorithm to adjust our approach in different scenarios, thereby improving its generalization ability;2) it automatically learns a proper weight for each view, thereby reducing the influence of noises;3) it performs doubly soft regularized regression that aligns the same instances in different views, thereby decreasing the impact of missing instances. Extensive experimental results show its superior advantages over other state-of-the-art works. © 2020 IEEE.

关键词： Clustering algorithms

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Contrastive Learning for Robust Android Malware Familial Classification

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IEEE Transactions on Dependable and Secure Computing 2022年 1-14页

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China University of Texas at Dallas Dallas USA National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. IEEE

关键词： Semantics

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Tracking Objects and Activities with Attention for Temporal Sentence Grounding 48

Tracking Objects and Activities with Attention for Temporal ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Peking University Wangxuan Institute of Computer Technology China

ISBN: (纸本)9781728163277

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. © 2023 IEEE.

关键词： Computer vision

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