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2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering, ICPCSI 2017

作者： Rafiuddin, Mohammed Farook Bin Minhas, Hamza Dhubb, Prethpal Singh Forensics Computing and Security Asia Pacific University Malaysia

ISBN: (纸本)9781538608135

The following is a research conducted on the Dark Web to study and identify the ins and outs of the dark web, what the dark web is all about, the various methods available to access the dark web and many others. The researchers have also included the steps and precautions taken before the dark web was opened. Apart from that, the findings and the website links / URL are also included along with a description of the sites. The primary usage of the dark web and some of the researcher's experience has been further documented in this research paper. © 2017 IEEE.

关键词： Malware

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Research on theory and key technology of trusted computing platform security testing and evaluation

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Science China(Information Sciences) 2010年第3期53卷 434-453页

作者： ZHANG HuanGuo1,2, YAN Fei1,2, FU JianMing1,2, XU MingDi1,2, YANG Yang1,2, HE Fan1,2 & ZHAN Jing1,2 1School of Computer, Wuhan University, Wuhan 430072, China 2Key Laboratory of Aerospace Information security and Trusted computing, Ministry of Education, Wuhan 430072, China School of Computer Wuhan University Wuhan China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan China

Trusted computing has become a new trend in the area of international information security, and the products of trusted computing platform begin to be used in application. Users will not use the products of information security, unless it goes through the testing and evaluation. Here we concentrate on the testing and evaluation problem of trusted computing platform, begin with constructing proper formalization model of trusted computing platform for testing, and establish a mathematical chain of trust model based on SPA. Moreover, we give a verification method of composite characteristics and find the potential factors threatening the trusted system in the process of remote attestation through analysis. For trusted software stack, we study the problem of automatic generation of test case and propose an improved method of generating the random test case, to raise the quality of test case. Finally, we give a prototype system of trusted computing platform and the actual test data related. The result demonstrates that there exist some flaws in the architecture of the present TCG computing platform. At the same time, some flaws are found in the products of existing trusted computing platform, thus a basis is laid for the improvement and development of trusted platform technology and its products.

关键词： information security trusted computing trusted computing platform testing and evaluation

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Fairness is essential for robustness:fair adversarial training by identifying and augmenting hard examples

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Frontiers of Computer Science 2025年第3期19卷 1-13页

作者： Ningping MOU Xinli YUE Lingchen ZHAO Qian WANG Key Laboratory of Aerospace Information Security and Trusted Computing(Ministry of Education) School of Cyber Science and EngineeringWuhan UniversityWuhan 430072China

Adversarial training has been widely considered the most effective defense against adversarial ***,recent studies have demonstrated that a large discrepancy exists in the class-wise robustness of adversarial training,leading to two potential issues:firstly,the overall robustness of a model is compromised due to the weakest class;and secondly,ethical concerns arising from unequal protection and biases,where certain societal demographic groups receive less robustness in defense *** these issues,solutions to address the discrepancy remain largely *** this paper,we advance beyond existing methods that focus on class-level *** investigation reveals that hard examples,identified by higher cross-entropy values,can provide more fine-grained information about the ***,we find that enhancing the diversity of hard examples can effectively reduce the robustness gap between *** by these observations,we propose Fair Adversarial Training(FairAT)to mitigate the discrepancy of class-wise *** experiments on various benchmark datasets and adversarial attacks demonstrate that FairAT outperforms state-of-the-art methods in terms of both overall robustness and *** a WRN-28-10 model trained on CIFAR10,FairAT improves the average and worst-class robustness by 2.13%and 4.50%,respectively.

关键词： robust fairness adversarial training hard example data augmentation

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Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G Networks

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IEEE Communications Surveys and Tutorials 2025年第2期27卷 748-781页

作者： Zhu, Xiaoqiang Liu, Jiqiang Lu, Lingyun Zhang, Tao Qiu, Tie Wang, Chunpeng Liu, Yuan Beijing Jiaotong University School of Software Engineering Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing100044 China Tianjin University College of Intelligence and Computing Tianjin300350 China Qilu University of Technology School of Cyber Security and the Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan250353 China

The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support "intelligent connectivity" across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks. © 1998-2012 IEEE.

关键词： Computer architecture

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Explicitly Disentangling and Exclusively Fusing for Semi-Supervised Bi-Modal Salient Object Detection

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4479-4492页

作者： Wang, Jie Kong, Xiangji Yu, Nana Zhang, Zihao Han, Yahong Tianjin University College of Intelligence and Computing Tianjin300072 China Zhengzhou University School of Cyberspace Security Zhengzhou450001 China

Bi-modal (RGB-T and RGB-D) salient object detection (SOD) aims to enhance detection performance by leveraging the complementary information between modalities. While significant progress has been made, two major limitations persist. Firstly, mainstream fully supervised methods come with a substantial burden of manual annotation, while weakly supervised or unsupervised methods struggle to achieve satisfactory performance. Secondly, the indiscriminate modeling of local detailed information (object edge) and global contextual information (object body) often results in predicted objects with incomplete edges or inconsistent internal representations. In this work, we propose a novel paradigm to effectively alleviate the above limitations. Specifically, we first enhance the consistency regularization strategy to build a basic semi-supervised architecture for the bi-modal SOD task, which ensures that the model can benefit from massive unlabeled samples while effectively alleviating the annotation burden. Secondly, to ensure detection performance (i.e., complete edges and consistent bodies), we disentangle the SOD task into two parallel sub-tasks: edge integrity fusion prediction and body consistency fusion prediction. Achieving these tasks involves two key steps: 1) the explicitly disentangling scheme decouples salient object features into edge and body features, and 2) the exclusively fusing scheme performs exclusive integrity or consistency fusion for each of them. Eventually, our approach demonstrates significant competitiveness compared to 26 fully supervised methods, while effectively alleviating 90% of the annotation burden. Furthermore, it holds a substantial advantage over 15 non-fully supervised methods. © 1991-2012 IEEE.

关键词： Self-supervised learning

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Three-in-One: Robust Enhanced Universal Transferable Anti-Facial Retrieval in Online Social Networks

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IEEE Transactions on Information Forensics and security 2025年 20卷 4408-4421页

作者： Lv, Yunna Tang, Long Ye, Dengpan Xie, Caiyun Deng, Jiacheng He, Yiheng Shen, Sipeng Wuhan University School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education Wuhan430072 China

Deep hash-based retrieval techniques are widely used in facial retrieval systems to improve the efficiency of facial matching. However, it also carries the danger of exposing private information. Deep hash models are easily influenced by adversarial examples, which can be leveraged to protect private images from malicious retrieval. The existing adversarial example methods against deep hash models focus on universality and transferability, lacking the research on its robustness in online social networks (OSNs), which leads to their failure in anti-retrieval after post-processing. Therefore, we provide the first in-depth discussion on robustness in universal transferable anti-facial retrieval and propose Three-in-One Adversarial Perturbation (TOAP). Specifically, we construct a local and global Compression Generator (CG) to simulate complex post-processing scenarios, which can be used to mitigate perturbation. Then, we propose robust optimization objectives based on the discovery of the variation patterns of model’s distribution after post-processing, and generate adversarial examples using these objectives and meta-learning. Finally, we iteratively optimize perturbation by alternately generating adversarial examples and fine-tuning the CG, balancing the performance of perturbation while enhancing CG’s ability to mitigate them. Numerous experiments demonstrate that, in addition to its advantages in universality and transferability, TOAP significantly outperforms current state-of-the-art methods in multiple robustness metrics. It further improves universality and transferability by 5% to 28%, and achieves up to about 33% significant improvement in several simulated post-processing scenarios as well as mainstream OSNs, demonstrating that TOAP can effectively protect private images from malicious retrieval in real-world scenarios. © 2005-2012 IEEE.

关键词： Social networking (online)

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Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection

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ACM Transactions on Privacy and security 2025年第2期28卷 1-32页

作者： Bostani, Hamid Zhao, Zhengyu Liu, Zhuoran Moonsamy, Veelasha Digital Security Group Institute for Computing and Information Sciences Radboud University Nijmegen Netherlands Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China Horst Görtz Institute for It Security Ruhr University Bochum Bochum Germany

Machine Learning (ML) promises to enhance the efficacy of Android Malware Detection (AMD);however, ML models are vulnerable to realistic evasion attacks - crafting realizable Adversarial Examples (AEs) that satisfy Android malware domain constraints. To eliminate ML vulnerabilities, defenders aim to identify susceptible regions in the feature space where ML models are prone to deception. The primary approach to identifying vulnerable regions involves investigating realizable AEs, but generating these feasible apps poses a challenge. For instance, previous work has relied on generating either feature-space norm-bounded AEs or problem-space realizable AEs in adversarial hardening. The former is efficient but lacks full coverage of vulnerable regions, whereas the latter can uncover these regions by satisfying domain constraints but is known to be time consuming. To address these limitations, we propose an approach to facilitate the identification of vulnerable regions. Specifically, we introduce a new interpretation of Android domain constraints in the feature space, followed by a novel technique that learns them. Our empirical evaluations across various evasion attacks indicate effective detection of AEs using learned domain constraints, with an average of 89.6%. Furthermore, extensive experiments on different Android malware detectors demonstrate that utilizing our learned domain constraints in adversarial training outperforms other adversarial training based defenses that rely on norm-bounded AEs or state-of-the-art non-uniform perturbations. Finally, we show that retraining a malware detector with a wide variety of feature-space realizable AEs results in a 77.9% robustness improvement against realizable AEs generated by unknown problem-space transformations, with up to 70× faster training than using problem-space realizable AEs. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Android malware

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Diffusion Model with Multi-layer Wavelet Transform for Low-Light Image Enhancement

Diffusion Model with Multi-layer Wavelet Transform for Low-L...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jin, Haiyan Wang, Jing Zuo, Fengyuan Su, Haonan Xiao, Zhaolin Wang, Bin Zhang, Yuanlin Xi'an University of Technology 710048 China Shaanxi Key Laboratory for Network Computing and Security Technology 710048 China

ISBN: (纸本)9798350368741

Low-light image enhancement methods based on diffusion models, though effective in improving image quality, often overrely on noise sensitivity and neglect the reconstruction deviations due to the naive up- and down-sampling operations. To address this issue, we propose a novel diffusion model, MWT-Diff, which utilizes multi-layer wavelet transforms to replace up- and down-sampling based on convolutions for extracting high-order features of different scales while mitigating representation degradations. Specifically, MWT-Diff is based on the U-Net architecture;it encodes four local features after the frequency-based down-sampling at each layer and fuses the enhanced four components during the up-sampling process. Additionally, we incorporate global refinement branches to mitigate information loss and employ efficient soft gate aggregation for feature fusion and reconstruction. Extensive quantitative and qualitative experiments demonstrate that our model achieves state-of-the-art performance on benchmark datasets. Code is available at: https://***/lalalulao/MWT-Diff. © 2025 IEEE.

关键词： Diffusion Model Low-light Image Enhancement Multi-layer Wavelet Transform Soft Gate Mechanism

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Efficient Multi-Keyword Searchable and Verifiable Data Sharing for Cloud-Edge Collaboration Intelligent Transportation Systems

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IEEE Internet of Things Journal 2025年第11期12卷 15869-15882页

作者： Liu, Peng He, Qian Chen, Yiting Jiang, Shan Guilin University of Electronic Technology Department of Guangxi Key Laboratory of Cryptograph and Information Security Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Cryptograph and Information Security Guilin541004 China The Hong Kong Polytechnic University Department of Computing Hong Kong

Intelligent transportation systems (ITS) are essential for the development of future smart cities. They can improve traffic management, mitigate urban congestion, and provide extensive social services by disseminating traffic data collected from *** facilitate the exchange of sensory data with other vehicles and alleviate the local storage load, sensory data from vehicles is frequently uploaded to cloud or edge ***, current ITS data sharing frameworks exhibit certain security vulnerabilities. In particular, they are unable to support secure multi-keyword searches or ensure verifiable retrieval of encrypted information. Moreover, some of the schemes cannot ensure the integrity of the retrieved data. Therefore, we propose an efficient multi-keyword searchable and verifiable data sharing(EMKV-ABSE) for cloud-edge collaboration ITS. EMKV-ABSE integrates a multi-keyword attribute-based searchable encryption scheme and a tamper-proof consortium blockchain. In EMKV-ABSE, we store the keyword indexes and ciphertext hashes of the shared data on the blockchain, which can ensure the integrity and authenticity of the data. Meanwhile, to avoid security and trust issues associated with central search cloud servers, smart contracts are utilized to perform multi-keyword searches and verify retrieval results. Furthermore, the EMKV-ABSE scheme outsources the complex computational tasks in encryption and decryption to edge servers, which realizes lightweight computation for resource-constrained *** security analysis proves that EMKV-ABSE satisfies the indistinguishability under the chosen plaintext attack (IND-CPA) and the chosen keyword attack (IND-CKA) in the standard *** evaluation shows the efficiency and practicability of our scheme. © 2014 IEEE.

关键词： Data integrity

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FlexFL: Flexible and Effective Fault Localization with Open-Source Large Language Models

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IEEE Transactions on Software Engineering 2025年第5期51卷 1455-1471页

作者： Xu, Chuyang Liu, Zhongxin Ren, Xiaoxue Zhang, Gehao Liang, Ming Lo, David Zhejiang University State Key Laboratory of Blockchain and Data Security Hangzhou310027 China Ant Group China Singapore Management University School of Computing and Information Systems 188065 Singapore

Fault localization (FL) targets identifying bug locations within a software system, which can enhance debugging efficiency and improve software quality. Due to the impressive code comprehension ability of Large Language Models (LLMs), a few studies have proposed to leverage LLMs to locate bugs, i.e., LLM-based FL, and demonstrated promising performance. However, first, these methods are limited in flexibility. They rely on bug-triggering test cases to perform FL and cannot make use of other available bug-related information, e.g., bug reports. Second, they are built upon proprietary LLMs, which are, although powerful, confronted with risks in data privacy. To address these limitations, we propose a novel LLM-based FL framework named FlexFL, which can flexibly leverage different types of bug-related information and effectively work with open-source LLMs. FlexFL is composed of two stages. In the first stage, FlexFL reduces the search space of buggy code using state-of-the-art FL techniques of different families and provides a candidate list of bug-related methods. In the second stage, FlexFL leverages LLMs to delve deeper to double-check the code snippets of methods suggested by the first stage and refine fault localization results. In each stage, FlexFL constructs agents based on open-source LLMs, which share the same pipeline that does not postulate any type of bug-related information and can interact with function calls without the out-of-the-box capability. Extensive experimental results on Defects4J demonstrate that FlexFL outperforms the baselines and can work with different open-source LLMs. Specifically, FlexFL with a lightweight open-source LLM Llama3-8B can locate 42 and 63 more bugs than two state-of-the-art LLM-based FL approaches AutoFL and AgentFL that both use GPT-3.5. In addition, FlexFL can localize 93 bugs that cannot be localized by non-LLM-based FL techniques at the top 1. Furthermore, to mitigate potential data contamination, we conduct experiment

关键词： Program debugging

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