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Enhance information Flow Tracking with Function Recognition

China Communications 2010年第6期7卷 24-29页

作者： Zhou Kan Huang Shiqiu Huang Shan Qi Zhengwei Gu Jian Guan Haibing School of software Shanghai Jliao Tong University Shanghai 200240 P. R. China Key Lab of Information Network Security Ministry of Public Security Department of Computer Science ＆ Engineering Shanghai Jiao Tong University Shanghai 200240 P. R China Shanghai Key Laboratory of Scalable Computing ＆ System Shanghai P. R. China

With the spread use of the computers, a new crime space and method are presented for criminals. Thus computer evidence plays a key part in criminal cases. Traditional computer evidence searches require that the computer specialists know what is stored in the given computer. Binary-based information flow tracking which concerns the changes of control flow is an effective way to analyze the behavior of a program. The existing systems ignore the modifications of the data flow, which may be also a malicious behavior. Thus the function recognition is introduced to improve the information flow tracking. Function recognition is a helpful technique recognizing the function body from the software binary to analyze the binary code. And that no false positive and no false negative in our experiments strongly proves that our approach is effective.

关键词： function recognition information flow tracking program behavior e-evidence.

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Threshold Signature Scheme Based on Non-internative Diffie-Hellman Signature

Threshold Signature Scheme Based on Non-internative Diffie-H...

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International Conference on networking and Distributed computing (ICNDC)

作者： Xiuqun Wang Lican Huang Key Laboratory of Information System Security Ministry of Education Tsinghua University Beijing China Institute of Network and Distributed Computing Zhejiang Science and Technology University Hangzhou China

We present a threshold signature based on non-internative Diffie-Hellman signature. It works in asynchronous communication system and can tolerate Byzantine faults with optimal resilience n ≥ = 3t + 1, where n is the number of the participants and t is the number of the fault participants. The security of this scheme bases on discrete logarithm intractability assumption. We give the security analysis of our proposed threshold signature scheme.

关键词： Polynomials Protocols Public key Interpolation Robustness

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SkyF2F: Censorship Resistant via Skype Overlay network

SkyF2F: Censorship Resistant via Skype Overlay Network

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WASE International Conference on information Engineering, ICIE

作者： Shoufeng Cao Longtao He Zhongxian Li Yixian Yang Information Security Center State Key Laboratory of Networking and Switching Technology Key Laboratory of network and information attack & defence technology of MOE Beijing University of Posts and Telecommunications China Research Center of Information Security Institute of Computing Technology Chinese Academy and Sciences Beijing China

Internet censorship is on the rise as an increasing number of countries and companies block or monitor access to parts of the Internet. Many censorship resistant systems have been proposed, which rely on deploying many access points to the censored domain. Therefore they face the problem of discovering available nodes and deploying a large number of nodes. Opposite to many access point approach, we present a system building on existing overlay network, a low-cost solution for circumventing Internet, called SkyF2F. SkyF2F is a plug-in for Skype client that allows users to establish a covert communication tunnel across Skype overlay network. We describe the design, a prototype implementation and security analysis of SkyF2F. Our security analysis shows that SkyF2F can successfully circumvent several sophisticated censoring techniques.

关键词： information security Internet Computer security National security Laboratories Cryptography Buildings Prototypes Monitoring IP networks

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Quick Finding of network Video Stream

Quick Finding of Network Video Stream

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International Conference on Computer Science and information Technology (CSIT)

作者： Qindong Sun Shengli Li Xiaojun Guo Network Computing and Security Key Laboratory of Shaanxi Xi'an University of Technology Xi'an China School of Electronics and Information Engineering Xi'an Jiaotong University Xi'an China Department of Research and Development Xi'an Anzhi Science and Technology Corporation Xi'an China

network video technology develops quickly in recent years and the applications of network video are various. The effectively managing of the application based on network video is necessary for network flow management, network security, etc. and finding video stream from the network flow is the precondition and foundation. This paper analyzes the characteristics of the video stream packets, and draws a conclusion that each network video stream type can be labeled uniquely by one characteristic string, based on which, we can find video streams in network flow easily. The experiment results show that the method is able to find network video streams accurately.

关键词： Streaming media Protocols Decision support systems Payloads Servers IP networks Internet

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A Cache Considering Role-Based Access Control and Trust in Privilege Management Infrastructure

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Wuhan University Journal of Natural Sciences 2006年第6期11卷 1827-1830页

作者： ZHANG Shaomin WANG Baoyi ZHOU Lihua Key Laboratory of Computer Network and Information Security Ministry of Education Xidian University Xi＇an 710071 Shaanxi China School of Computer North China Electric Power University Baoding 071003 Hebei China

PMI （privilege management infrastructure） is used to perform access control to resource in an E-commerce or E-government system. With the ever-increasing need for secure transaction, the need for systems that offer a wide variety of QoS （quality-of-service） features is also growing. In order to improve the QoS of PMI system, a cache based on RBAC （Role-based Access control） and trust is proposed. Our system is realized based on Web service. How to design the cache based on RBAC and trust in the access control model is deseribed in detail. The algorithm to query role permission in cache and to add records in cache is dealt with. The policy to update cache is introduced also.

关键词： access control RBAC（role-based access controd trust cache PMI （privilege management infrastructure）

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A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

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ACM computing Surveys 1000年

作者： Md Palash Uddin Yong Xiang Mahmudul Hasan Jun Bai Yao Zhao Longxiang Gao School of Information Technology Deakin University Burwood Australia Deakin University Burwood Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.

关键词： Federated Learning Robustness Attack Countermeasure Defence.

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IBAQ: Frequency-Domain Backdoor Attack Threatening Autonomous Driving via Quadratic Phase

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jinghan Qiu Honglong Chen Junjian Li Yudong Gao Junwei Li Xingang Wang College of Control Science and Engineering China University of Petroleum China the Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China

The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be activated to manipulate the model’s inference. Backdoor attacks can lead to severe consequences, such as misidentifying traffic signs during autonomous driving, posing a risk of causing traffic accidents. Recently, there has been a gradual evolution of frequency-domain backdoor attacks. However, since the change of both amplitude and its corresponding phase will significantly affect image appearance, most of the existing frequency-domain backdoor attacks change only the amplitude, which results in a suboptimal efficacy of the attack. In this work, we propose an attack called IBAQ, to solve this problem by blurring semantic information of the trigger image through the quadratic phase. Initially, we convert the trigger and benign sample to YCrCb space. Then, we perform the fast Fourier transform on the Y channel, blending the trigger image’s amplitude and quadratic phase linearly with the benign sample’s amplitude and phase. IBAQ achieves covert injection of trigger information within amplitude and phase, enhancing the attack effect. We validate the effectiveness and stealthiness of IBAQ through comprehensive experiments.

关键词： backdoor attack quadrature phase artificial intelligence security

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S3 Agent: Unlocking the power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

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ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Peng Wang Yongheng Zhang Hao Fei Qiguang Chen Yukai Wang Jiasheng Si Wenpeng Lu Min Li Libo Qin School of Computer Science and Engineering Central South University China National University of Singapore Singapore Harbin Institute of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression. Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.

关键词： Natural language processing Multi-modal sarcasm detection Vision Large language model

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Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation

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ACM Transactions on information Systems 1000年

作者： Ziang Lu Lei Guo Xu Yu Zhiyong Cheng Xiaohui Han Lei Zhu Shandong Normal University China China University of Petroleum (East China) China Hefei University of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China Tongji University China

In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-Domain Recommendation (CDR), especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: 1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. 2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. 3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items’ semantic encodings from their original texts by a multi-layer semantic encoder, and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform (FFT)-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at: https://***/Sapphire-star/FFMSR.

关键词： Cross-domain Recommendation Federated Learning Semantic Fusion Semantic Filter

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Semantic-enhanced Co-attention Prompt Learning for Non-overlapping Cross-Domain Recommendation

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ACM Transactions on information Systems 1000年

作者： Lei Guo Chenlong Song Feng Guo Xiaohui Han Xiaojun Chang Lei Zhu Shandong Normal University China Liaocheng University China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences) China and Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science China Tongji University China

Non-overlapping Cross-domain Sequential Recommendation (NCSR) is the task that focuses on domain knowledge transfer without overlapping entities. Compared with traditional Cross-domain Sequential Recommendation (CSR), NCSR poses several challenges: 1) NCSR methods often rely on explicit item IDs, overlooking semantic information among entities. 2) Existing CSR mainly relies on domain alignment for knowledge transfer, risking semantic loss during alignment. 3) Most previous studies do not consider the many-to-one characteristic, which is challenging because of the utilization of multiple source domains. Given the above challenges, we introduce the prompt learning technique for Many-to-one Non-overlapping Cross-domain Sequential Recommendation (MNCSR) and propose a Text-enhanced Co-attention Prompt Learning Paradigm (TCPLP). Specifically, we capture semantic meanings by representing items through text rather than IDs, leveraging natural language universality to facilitate cross-domain knowledge transfer. Unlike prior works that need to conduct domain alignment, we directly learn transferable domain information, where two types of prompts, i.e., domain-shared and domain-specific prompts, are devised, with a co-attention-based network for prompt encoding. Then, we develop a two-stage learning strategy, i.e., pre-train & prompt-tuning paradigm, for domain knowledge pre-learning and transferring, respectively. We conduct extensive experiments on three datasets and the experimental results demonstrate the superiority of our TCPLP. Our source codes have been publicly released.

关键词： Cross-domain recommendation sequential recommendation non-overlapping cross-domain recommendation

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