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PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature

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PS3: Precise Patch Presence Test Based on Semantic Symbolic ...

International Conference on Software Engineering (ICSE)

作者： Qi Zhan Xing Hu Zhiyang Li Xin Xia David Lo Shanping Li The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei China Singapore Management University Singapore

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named $PS^{3}$ , referring to precise patch presence test based on semantic-level symbolic signature. $PS^{3}$ exploits symbolic emulation to extract signatures that are stable under different compiler options. Then $PS^{3}$ can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that $PS^{3}$ achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. $PS^{3}$ outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options.

关键词： Program processors Semantics Emulation Software systems security Testing Software engineering

Fast Algorithm for Parallel Solving Inversion of Large Scale Small Matrices Based on Gpu

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SSRN

SSRN 2022年

作者： Jin, Xuebin Chen, Yewang Fan, Wentao Zhang, Yong Du, Jixiang The College of Computer Science and Technology Huaqiao University Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Soochow China College of Mechanical Engineering and Automation Huaqiao University Xiamen China

Inverting a matrix is time-consuming, and many works focus on accelerating inverting a single large matrix by GPU. However, the problem of inverting large-scale small matrices has little attention. In this paper, we propose a Revised In-Place Inversion algorithm for inverting large-scale small matrices, which runs on the CUDA platform and utilizes multicore GPUs. The proposed algorithm is well-suited for large-scale-small matrices (dimension $n \leq 32$), which can invert millions of small matrices within tens of milliseconds. In addition, we find there is an upper bound of input data size for each GPU device, the performance will degrade if the input data size is too large. Hence, we improve the proposed method by dividing matrices into batches according to this finding. Experimental results show that this strategy can effectively alleviate the performance degradation for inverting large scale small matrices, and maintain the high-performance execution of the algorithm. © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

Preventing the Popular Item Embedding Based Attack in Federated Recommendations

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Preventing the Popular Item Embedding Based Attack in Federa...

International Conference on data Engineering

作者： Jun Zhang Huan Li Dazhong Rong Yan Zhao Ke Chen Lidan Shou The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science Aalborg University Aalborg Denmark

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense.

关键词： Training Privacy data engineering Robustness Recommender systems

Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Model

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arXiv 2024年

作者： Cui, Yu Liu, Feng Wang, Pengbo Wang, Bohao Tang, Heng Wan, Yi Wang, Jun Chen, Jiawei Zhejiang University Hangzhou China OPPO Research Institute Shenzhen China University of Electronic Science and Technology of China Chengdu China Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China

Owing to their powerful semantic reasoning capabilities, Large Language Models (LLMs) have been effectively utilized as recommenders, achieving impressive performance. However, the high inference latency of LLMs significantly restricts their practical deployment. To address this issue, this work investigates knowledge distillation from cumbersome LLM-based recommendation models to lightweight conventional sequential models. It encounters three challenges: 1) the teacher’s knowledge may not always be reliable;2) the capacity gap between the teacher and student makes it difficult for the student to assimilate the teacher’s knowledge;3) divergence in semantic space poses a challenge to distill the knowledge from embeddings. To tackle these challenges, this work proposes a novel distillation strategy, DLLM2Rec, specifically tailored for knowledge distillation from LLM-based recommendation models to conventional sequential models. DLLM2Rec comprises: 1) Importance-aware ranking distillation, which filters reliable and student-friendly knowledge by weighting instances according to teacher confidence and student-teacher consistency;2) Collaborative embedding distillation integrates knowledge from teacher embeddings with collaborative signals mined from the data. Extensive experiments demonstrate the effectiveness of the proposed DLLM2Rec, boosting three typical sequential models with an average improvement of 47.97%, even enabling them to surpass LLM-based recommenders in some cases. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information

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Ethereum Phishing Detection Using Hyperbolic Neural Networks...

International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Rong Xu Jun Zhang Xiaoyue Zhu Zhaoxiong Song Zhi Yu College of Computer Science Inner Mongolia University Hohhot China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness.

关键词： Attention mechanisms Phishing Neural networks Benchmark testing Feature extraction Real-time systems blockchains Safety security Tuning

VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals

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arXiv 2024年

作者： Singh, Alakhsimar Verma, Nischay Goyal, Kanav Singh, Amritpal Kumar, Puneet Li, Xiaobai Computer Science and Engineering Department National Institute of Technology Jalandhar India Center for Machine Vision and Signal Analysis University of Oulu Finland The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China

This paper presents VisioPhysioENet, a novel multimodal system that leverages visual and physiological signals to detect learner engagement. It employs a two-level approach for extracting both visual and physiological features. For visual feature extraction, Dlib is used to detect facial landmarks, while OpenCV provides additional estimations. The face_recognition library, built on Dlib, is used to identify the facial region of interest specifically for physiological signal extraction. Physiological signals are then extracted using the plane-orthogonal-to-skin method to assess cardiovascular activity. These features are integrated using advanced machine learning classifiers, enhancing the detection of various levels of engagement. We thoroughly tested VisioPhysioENet on the DAiSEE dataset. It achieved an accuracy of 63.09%. This shows it can better identify different levels of engagement compared to many existing methods. It performed 8.6% better than the only other model that uses both physiological and visual features. Copyright © 2024, The Authors. All rights reserved.

关键词： Physiological models

Towards Efficient Asynchronous Federated Learning in Heterogeneous Edge Environments

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Towards Efficient Asynchronous Federated Learning in Heterog...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yajie Zhou Xiaoyi Pang Zhibo Wang Jiahui Hu Peng Sun Kui Ren The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China School of Cyber Science and Technology Zhejiang University Hangzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated learning (FL) is widely used in edge environments as a privacy-preserving collaborative learning paradigm. However, edge devices often have heterogeneous computation capabilities and data distributions, hampering the efficiency of co-training. Existing works develop staleness-aware semi-asynchronous FL that reduces the contribution of slow devices to the global model to mitigate their negative impacts. But this makes data on slow devices unable to be fully leveraged in global model updating, exacerbating the effects of data heterogeneity. In this paper, to cope with both system and data heterogeneity, we propose a clustering and two-stage aggregation-based Efficient Asynchronous Federated Learning (EAFL) framework, which can achieve better learning performance with higher efficiency in heterogeneous edge environments. In EAFL, we first propose a gradient similarity-based dynamic clustering mechanism to cluster devices with similar system and data characteristics together dynamically during the training process. Then, we develop a novel two-stage aggregation strategy consisting of staleness-aware semi-asynchronous intra-cluster aggregation and data size-aware synchronous inter-cluster aggregation to efficiently and comprehensively aggregate training updates across heterogeneous clusters. With that, the negative impacts of slow devices and Non-IID data can be simultaneously alleviated, thus achieving efficient collaborative learning. Extensive experiments demonstrate that EAFL is superior to state-of-the-art methods.

关键词： Training Performance evaluation Adaptation models Federated learning Computational modeling Aggregates Collaboration

When ChatGPT Meets Smart Contract Vulnerability Detection: How Far Are We?

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arXiv 2023年

作者： Chen, Chong Su, Jianzhong Chen, Jiachi Wang, Yanlin Bi, Tingting Yu, Jianxing Wang, Yanli Lin, Xingwei Chen, Ting Zheng, Zibin Sun Yat-sen University China The State Key Laboratory of Blockchain and Data Security Zhejiang University China University of Western Australia Australia Zhejiang University China University of Electronic Science and Technology of China China

With the development of blockchain technology, smart contracts have become an important component of blockchain applications. Despite their crucial role, the development of smart contracts may introduce vulnerabilities and potentially lead to severe consequences, such as financial losses. Meanwhile, large language models, represented by ChatGPT, have gained great attentions, showcasing great capabilities in code analysis tasks. In this paper, we presented an empirical study to investigate the performance of ChatGPT in identifying smart contract vulnerabilities. Initially, we evaluated ChatGPT’s effectiveness using a publicly available smart contract dataset. Our findings discover that while ChatGPT achieves a high recall rate, its precision in pinpointing smart contract vulnerabilities is limited. Furthermore, ChatGPT’s performance varies when detecting different vulnerability types. We delved into the root causes for the false positives generated by ChatGPT, and categorized them into four groups. Second, by comparing ChatGPT with other state-of-the-art smart contract vulnerability detection tools, we found that ChatGPT’s F-score is lower than others for 3 out of the 7 vulnerabilities. In the case of the remaining 4 vulnerabilities, ChatGPT exhibits a slight advantage over these tools. Finally, we analyzed the limitation of ChatGPT in smart contract vulnerability detection, revealing that the robustness of ChatGPT in this field needs to be improved from two aspects: its uncertainty in answering questions;and the limited length of the detected code. In general, our research provides insights into the strengths and weaknesses of employing large language models, specifically ChatGPT, for the detection of smart contract vulnerabilities. Copyright © 2023, The Authors. All rights reserved.

关键词： Smart contract

BSL: Understanding and Improving Softmax Loss for Recommendation

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arXiv 2023年

作者： Wu, Junkang Chen, Jiawei Wu, Jiancan Shi, Wentao Zhang, Jizhi Wang, Xiang MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China Hefei China The State Key Laboratory of Blockchain and Data Security Zhejiang Unversity China Institute of Artificial Intelligence Institute of Dataspace Hefei Comprehensive National Science Center China

Loss functions steer the optimization direction of recommendation models and are critical to model performance, but have received relatively little attention in recent recommendation research. Among various losses, we find Softmax loss (SL) stands out for not only achieving remarkable accuracy but also better robustness and fairness. Nevertheless, the current literature lacks a comprehensive explanation for the efficacy of SL. Toward addressing this research gap, we conduct theoretical analyses on SL and uncover three insights: 1) Optimizing SL is equivalent to performing Distributionally Robust Optimization (DRO) on the negative data, thereby learning against perturbations on the negative distribution and yielding robustness to noisy negatives. 2) Comparing with other loss functions, SL implicitly penalizes the prediction variance, resulting in a smaller gap between predicted values and and thus producing fairer results. Building on these insights, we further propose a novel loss function Bilateral SoftMax Loss (BSL) that extends the advantage of SL to both positive and negative sides. BSL augments SL by applying the same Log-Expectation-Exp structure to positive examples as is used for negatives, making the model robust to the noisy positives as well. Remarkably, BSL is simple and easy-to-implement — requiring just one additional line of code compared to SL. Experiments on four real-world datasets and three representative backbones demonstrate the effectiveness of our proposal. The code is available at https://***/junkangwu/BSL. Copyright © 2023, The Authors. All rights reserved.

关键词： Recommender systems