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eSROP Attack: Leveraging Signal Handler to Implement Turing-Complete Attack Under CFI Defense 1

18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Zhang, Tianning Cai, Miao Zhang, Diming Huang, Hao Department of Computer Science and Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer and Information Hohai University Nanjing China College of Computer Engineering Jiangsu University of Science and Technology Nanjing China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Signal Return Oriented Programming (SROP) is a dangerous code reuse attack method. Recently, defense techniques have been proposed to defeat SROP attacks. In this paper, we leverage the signal nesting mechanism provided by current operating systems and propose a new variant of SROP attack called enhanced SROP (eSROP) attack. eSROP provides the ability of invoking arbitrary system calls, simulating Turing-complete computation, and even bypassing the fine-grained label-based CFI defense, without modifying the return address and instruction register in the signal frame. Because the signal returns to the interrupted instruction, the shadow stack defense can hardly detect our attack. Signal has strong flexibility which can interrupt the normal control flow. We leverage such flexibility to design a new code reuse attack. To evaluate eSROP, we perform two exploits on two real-world programs, namely Proftpd and Wu-ftpd. In our attacks, adversaries can invoke arbitrary system calls and obtain a root shell. Both attacks succeed within 10 min under strict system defense such as data execution prevention, address space layout randomization, and coarse-grained control flow integrity. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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Validity-Preserving Delta Debugging via Generator Trace Reduction

arXiv

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arXiv 2024年

作者： Ren, Luyao Zhang, Xing Hua, Ziyue Jiang, Yanyan He, Xiao Xiong, Yingfei Xie, Tao Key Laboratory of High Confidence Software Technologies Ministry of Education School of Computer Science Peking University Beijing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University China School of Computer and Communication Engineering University of Science and Technology Beijing China

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not satisfy the required specifications. This overall diminishing effectiveness and efficiency becomes even more pronounced when the specifications extend beyond syntactical structures. Our key insight is that we should leverage input generators, which are aware of these specifications, to generate valid reduced inputs, rather than straightforwardly performing reduction on test inputs. In this paper, we propose a generator-based delta debugging method, namely GReduce, which derives validity-preserving reducers. Specifically, given a generator and its execution, demonstrating how the bug-inducing test input is generated, GReduce searches for other executions on the generator that yield reduced, valid test inputs. The evaluation results on five benchmarks (i.e., graphs, DL models, JavaScript programs, SymPy, and algebraic data types) show that GReduce substantially outperforms state-of-the-art syntax-based reducers including Perses and T-PDD, and also outperforms QuickCheck, SmartCheck, as well as the state-of-the-art choice-sequence-based reducer Hypothesis, demonstrating the effectiveness, efficiency, and versatility of GReduce. Copyright © 2024, The Authors. All rights reserved.

关键词： software testing

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How Can We Cope with the Impact of Microservice Architecture Smells? 2022

How Can We Cope with the Impact of Microservice Architecture...

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11th International Conference on software and computer Applications, ICSCA 2022

作者： Ding, Xiang Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450385770

Context: software Architecture Smells (AS) are potential software structure problems and always impact software quality negatively. And with the development of Microservice architecture, the Microservice Architecture Smells (MAS) have been attracting more and more attention. The software scholars and developers have discovered the influence of MAS and performed some researches on them. However, the definition and category of MAS are still ambiguous. Objects: This paper aims to clarify the specific MAS categories and their definitions, and then further explores the issues caused by MAS in the migration process from a monolithic system to Microservice. Method: We performed a comprehensive systematic literature review about MAS. Specifically, we explored 13 white and 10 grey literature in detail to get MAS information by using the quantitative research method. To explore the issues that influence the migration process from a monolithic system to Microservice, we used the meta-ethnography qualitative research method to extract relevant information and get six three-order translations. Results: This study defined 22 Microservice Architecture Smells and classified them into five categories, namely Design, Deployment, Monitor & Log, Communication and Team & Tool, based on their characteristics. Simultaneously, the issues that influence the migration process are proposed, including service cutting, databases, communication, team and techniques. Finally, we matched the MAS to the issues they caused in the migration process and recommended solutions to these issues. © 2022 ACM.

关键词： meta-ethnography Microservice Architecture Smells smell impact Systematic Literature Review

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Implementation of Domain-oriented Microservices Decomposition based on Node-attributed Network 2022

Implementation of Domain-oriented Microservices Decompositio...

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11th International Conference on software and computer Applications, ICSCA 2022

作者： Cao, Lingli Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Anhui University China School of Computer Science and Technology Anhui University China State Key Lab for Novel Software Technology Nanjing University China

ISBN: (纸本)9781450385770

The features of microservices, such as scalability and maintainability, have attracted the industry to migrate monolithic projects to microservices. However, how to decompose microservices during migration is a tricky problem. At present, microservices decomposition mainly relies on architects or domain experts, which is more subjective and time-consuming. Followed by semi-automated or automated microservice decomposition, such methods produce coarse-grained results affected by different system characteristics, which cannot make desired decomposition according to the migration requirements of domains. Therefore, this paper proposes a domain-oriented fine-grained microservices decomposition resolution scheme. It uses dynamic and static analysis to obtain the invocation relationships and invocation times between entity methods and the response time of entity methods to represent three main system characteristics concerned during the migration: function, inter-service communications, and performance. And express the above information of monolith by the node-attributed network. Then it uses the community detection algorithm and the proposed similar hierarchical clustering algorithm to complete objective and effective decomposition. Finally, the rationality and feasibility of the proposed approach are verified using the JPetStore case. © 2022 ACM.

关键词： Dynamic analysis Static analysis Microservices Decomposition Node-attributed Network Domain

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Enhancing Topic Interpretability for Neural Topic Modeling through Topic-wise Contrastive Learning

arXiv

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arXiv 2024年

作者： Gao, Xin Lin, Yang Li, Ruiqing Wang, Yasha Chu, Xu Ma, Xinyu Yu, Hailong Software Engineering Institution School of Computer Science Peking University Beijing China Key Lab of High Confidence Software Technologies Ministry of Education Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University China National Engineering Research Center of Software Engineering Peking University Beijing China

Data mining and knowledge discovery are essential aspects of extracting valuable insights from vast datasets. Neural topic models (NTMs) have emerged as a valuable unsupervised tool in this field. However, the predominant objective in NTMs, which aims to discover topics maximizing data likelihood, often lacks alignment with the central goals of data mining and knowledge discovery which is to reveal interpretable insights from large data repositories. Overemphasizing likelihood maximization without incorporating topic regularization can lead to an overly expansive latent space for topic modeling. In this paper, we present an innovative approach to NTMs that addresses this misalignment by introducing contrastive learning measures to assess topic interpretability. We propose a novel NTM framework, named ContraTopic, that integrates a differentiable regularizer capable of evaluating multiple facets of topic interpretability throughout the training process. Our regularizer adopts a unique topic-wise contrastive methodology, fostering both internal coherence within topics and clear external distinctions among them. Comprehensive experiments conducted on three diverse datasets demonstrate that our approach consistently produces topics with superior interpretability compared to state-of-the-art NTMs. © 2024, CC BY.

关键词： Contrastive Learning

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Towards Expanding Precise Timing for Collaborative Its with Deterministic Communications in 6G

Towards Expanding Precise Timing for Collaborative Its with ...

引用

IEEE Conference on Wireless Communications and Networking

作者： Hongxing Li Haotian Li Qianhan Gao Guochu Shou Yaqiong Liu Zhigang Guo Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

关键词： 6G mobile communication Wireless communication Cellular networks Uncertainty Accuracy Measurement uncertainty Transportation Delays Synchronization software defined networking

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SWISP: Distributed Convoy Mining via Sliding Window-based Indexing and Sub-track Partitioning

SWISP: Distributed Convoy Mining via Sliding Window-based In...

引用

International Conference on Data engineering

作者： Chenxu Wang Xin Yang Tianyi Li Jiaxing Wei Pinghui Wang Hongzhen Xiang Christain S. Jensen School of Software Engineering MOE Key Lab of INNS Xi'an Jiaotong University Xi'an China School of Software Engineering Xi'an Jiaotong University Xi'an China Department of Computer Science Aalborg University Aalborg Denmark MOE Key Lab of INNS Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

With the widespread deployment of location-aware mobile devices, a mass of trajectory data is being generated and collected. Mining co-movement patterns of people and vehicles from streaming and massive trajectory data has attracted much attention due to its wide applications in various fields. As a typical co-movement pattern, convoys describe objects moving together in consecutive timestamps. There are two challenges for efficient distributed convoy mining: object clustering and workload balancing. Clustering objects in each time snapshot is a time-consuming operation. In addition, on the basis of practical application scenarios, load balancing is an important consideration for distributed algorithms. To tackle the above challenges, we propose a novel method for distributed convoy mining via sliding window-based indexing and sub-track partitioning, abbreviated SWISP. We offer three major advancements. First, we develop a grid-based DBSCAN clustering algorithm named Grid-DBSCAN for distributed scenarios. It avoids the exhaustive calculation of pairwise distances for neighborhood search and thus improves computational efficiency in the clustering stage. Second, we propose a sliding window-based indexing scheme to filter out sub-tracks with less than $k$ consecutive time snapshots, significantly reducing the number of candidate sub-tracks for convoy mining. Third, we develop a distributed convoy mining algorithm based on sub-track partitioning. It exploits both temporal and spatial information of sub-tracks for data partitioning and solves the data skewness problem caused by uneven data distributions. We conduct extensive experiments on four real-world datasets. The experimental results show that our distributed algorithm can handle large-scale trajectory data and is more efficient than state-of-the-art approaches.

关键词： Clustering algorithms Filtering algorithms Load management Information filters Mobile handsets Trajectory Partitioning algorithms

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CorrMAE: Pre-training Correspondence Transformers with Masked Autoencoder

arXiv

引用

arXiv 2024年

作者： Liao, Tangfei Zhang, Xiaoqin Xiao, Guobao Li, Min Wang, Tao Ye, Mang School of Computer Science Wuhan University China College of Computer Science and Artificial Intelligence Wenzhou University China School of Electronics and Information Engineering Tongji University China State Key Lab for Novel Software Technology Nanjing University China

Pre-training has emerged as a simple yet powerful methodology for representation learning across various domains. However, due to the expensive training cost and limited data, pre-training has not yet been extensively studied in correspondence pruning. To tackle these challenges, we propose a pre-training method to acquire a generic inliers-consistent representation by reconstructing masked correspondences, providing a strong initial representation for downstream tasks. Toward this objective, a modicum of true correspondences naturally serve as input, thus significantly reducing pre-training overhead. In practice, we introduce CorrMAE, an extension of the mask autoencoder framework tailored for the pre-training of correspondence pruning. CorrMAE involves two main phases, i.e., correspondence learning and matching point reconstruction, guiding the reconstruction of masked correspondences through learning visible correspondence consistency. Herein, we employ a dual-branch structure with an ingenious positional encoding to reconstruct unordered and irregular correspondences. Also, a bi-level designed encoder is proposed for correspondence learning, which offers enhanced consistency learning capability and transferability. Extensive experiments have shown that the model pre-trained with our CorrMAE outperforms prior work on multiple challenging benchmarks. Meanwhile, our CorrMAE is primarily a task-driven pre-training method, and can achieve notable improvements for downstream tasks by pre-training on the targeted dataset. We hope this work can provide a starting point for correspondence pruning pre-training. © 2024, CC BY-NC-SA.

关键词： Signal encoding

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

引用

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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PPIRB:Achieving an privacy-preserving image retrieval scheme Based on Blockchain 5

PPIRB:Achieving an privacy-preserving image retrieval scheme...

引用

5th International Conference on Data Science and Information Technology, DSIT 2022

作者： Yuan, Bo Peng, Jiahui Li, Chunpei Qiu, Wangjie School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Guangxi Key Lab of Multi-source Information Mining Security Guangxi Normal University GuiLin China State Key Laboratory of Software Development Environment Beihang University Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

With the proliferation of cloud services, the work of large-scale image retrieval is carried out on remote cloud become a trend in order to get rid of the storage burden and computation. However, traditional retrieval work based on centralized cloud services is considered to take risk in giving away users' privacy in many researches. We need a secure way for retrieving and storing images by decentralized approach. In this paper, we propose a decentralized scheme named PPIRB(An Privacy-Preserving Image Retrieval Scheme Base on Blockchain) for retrieving images based on blockchain. We used the data structure called amplifying locality sensitive hashing (ALSH) to improve search accuracy. Searchable symmetric en-cryption(SSE) and ALSH used to realize the encryption retrieval of image index on the blockchain. Our scheme also uses IPFS to replace the cloud server to realize distributed storage, and uses secret sharing scheme to realize the access control. We implemented and evaluated the scheme in Ethereum, and the results show that our scheme is efficient and accurate in finding similar images while preserving privacy. The overall overhead is reasonable. © 2022 IEEE.

关键词： Blockchain

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