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IEEE International Conference on Trust, security and Privacy in Computing and Communications (TrustCom)

作者： Bin Yuan Shengyao Sun Xianjun Deng Deqing Zou Haoyu Chen Shenghui Li Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Research Institute Huazhong University of Science and Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab

ISBN: (纸本)9781665416597

The modern network consists of thousands of network devices from different suppliers that perform distinct code-pendent functions, such as routing, switching, modifying header fields, and access control across physical and virtual networks. Because of the network complexity, the network is prone to a wide range of errors, such as false-positive configuration, software errors, or unexpected interactions across protocols. These errors can lead to loops, sub-optimal routing, path leaks, black holes, and access control violations that make services unavailable, vulnerable to exploitation, or prone to attacks (e.g., DDoS attacks). To mitigate these problems, network operators deploy many different stateful network functions, like firewalls, NATs, load balancers, and intrusion-prevention boxes. They have become an important part of networks today, so it is critical to verify that these network functions are the same as expected deployments. All static network verification tools are meant to rigorously check network software or configuration for bugs before deployment. They usually use handwritten models or limited derivation models that are error-prone and ignore the fact that even the same type of network functions (from different vendors) still have different implementation details. In this paper, we propose a tool that can automatically synthesize more realistic and high-fidelity models that include stateful network functions with non-field attributes. We design an inferring algorithm, implement the transformation between data packages and symbolic packages, and obtain a finite state machine that can accurately express the actions of black-box network functions for a given configuration.

关键词： Access control Performance evaluation Privacy Protocols Computational modeling Switches Routing

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Robust and Secure Federated Learning with Verifiable Differential Privacy

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Chushan Weng, Jian Weng, Jiasi Zhong, Yijian Liu, Jia-Nan Deng, Cunle College of Cyber Security of Jinan University Guangzhou510632 China College of Cyber Security of Jinan University National Joint Engineering Research Center of Network Security Detection and Protection Technology Guangdong Key Laboratory of Data Security and Privacy Preserving Guangdong-Hong Kong-Macao Joint Laboratory of Data Security and Privacy Preserving Guangzhou510632 China Dongguan University of Technology School of Computer Science and Technology Dongguan523808 China

Federated Learning (FL) has emerged as a popular paradigm for training machine learning models on data from multiple sources without the need for data to leave their respective locations. Although FL is widely used, recent research indicates its vulnerability to data privacy breaches and Byzantine attacks. Addressing both threats simultaneously remains challenging, as previous defense mechanisms struggle to balance Byzantine resilience, data privacy, and training accuracy. Although combining Differential Privacy (DP) with Byzantineresilience in FL offers potential solutions, it presents challenges due to theoretical incompatibilities that can impact the performance of large-scale models. Addressing this underexplored challenge, we propose a novel approach that enhances privacy and Byzantine-resilient in FL while maintaining training accuracy. Our method integrates DP with Byzantine-resilience techniques, overcoming the limitations of existing methods that often sacrifice accuracy or even Byzantine resistance. Our experiments carefully analyze the impact of DP noise on training accuracy and Byzantine-resilience, guiding appropriate parameter selection. The experimental results demonstrate that our approach achieves superior global model accuracy compared to previous competitive methods, even with strong privacy constraints. © 2004-2012 IEEE.

关键词： Federated learning

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Network security Situational Awareness Model Based on Threat Intelligence 14th

Network Security Situational Awareness Model Based on Threat...

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14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Gao, Hongbin School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang05000 China

ISBN: (纸本)9783030898137

In order to deal with the problems that the increasing scale of the network in the real environment leads to the continuous high incidence of network attacks, the threat intelligence was applied to situational awareness, and the situational awareness model based on random game was constructed. Threat perception of the target system was performed by comparing the similarity between the exogenous threat intelligence and the internal security events of the system. At the same time, internal threat intelligence was generated based on the threat information inside the system. In this process, game theory was used to quantify the current network security situation of the system, evaluate the security status of the network. Finally, the prediction of the network security situation was realized. The experimental results show that the network security situation awareness method based on threat intelligence can reflect the changes in the network security situation and predict attack behaviors accurately. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Game theory

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Energy Optimization for Wild Animal Tracker Basing on Watchdog technology 7th

Energy Optimization for Wild Animal Tracker Basing on Watchd...

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7th International Conference on Artificial Intelligence and security, ICAIS 2021

作者： Xu, Zhenggang Zhai, Wenhan Liang, Chen Zhao, Yunlin Huang, Tian Zhou, Minghui Zhou, Libo Yang, Guiyan Hu, Zhiyuan School of Material and Chemical Engineering Hunan City University Yiyang413000 China Country College of Forestry Northwest A & F University Yangling712100 China Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security for Hunan Province Central South University of Forestry and Technology Changsha410004 China Engineering Research Center for Internet of Animals Changsha410000 China

ISBN: (纸本)9783030786113

Wild animal tracker, as an important IoT device terminal, can collect animal's position information and movement information in real time. The analysis based on the above big data provides us with a new perspective on recognizing animal behavior. It is useful in scientific research and animal management, while the device is always limited by energy. In order to develop the tracker device and enhance the energy supplying, wild animal tracker module including solar energy module, Maximum Power Point Tracking module, lithium battery, energy module, communication module, Global Positioning System module, data management module, stability controlling module, and ephemeris backup module etc. was designed in the study to optimize energy. Circuits for watchdog, energy management, solar energy protective were also designed in the novel tracker. Solar utilization efficiency and energy conversion efficiency have been enhanced after the energy optimization based on watchdog technology. The novel trackers were applied in many wild animals. Tests showed that the positioning accuracy, energy supplying, data collection all had been improved effectively. The tracker also proper worked in many harsh environments, such as desert and so on. The novel wild animal tracker can not only connect wild animals and the natural environment, but also human activities. There is also a huge market prospect, especially with the development of IoT technology and big data technology. © 2021, Springer Nature Switzerland AG.

关键词： Solar energy

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Trust and Risk Based on Access Control Model in Social Internet of Things

引用

International Journal of Network security 2023年第5期25卷 736-744页

作者： Zhang, Hongbin Liu, Jian Zhao, Dongmei Liu, Bin Wang, Yanmei Fan, Fan School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Scienc and Technology Shijiazhuang050000 China Hebei Geological Worker’s University Shijiazhuang050000 China

Integrating social networks and the Internet of Things has formed an emerging field of Social Internet of Things (IoT). Aiming to address the shortcomings of traditional TBAC (Trust based on Access Control) and ABAC (Attribute based on Access Control) in dynamic authorization and security, this paper proposes a hybrid access control model based on trust and risk for SIoT. The model supports social relationship-based trust assessment and risk prediction between SIoT nodes and integrates trust and risk values into SIoT security values through reasonable weight allocation. Furthermore, by simulating the safety requirements of different scenarios, it was verified that the model was efficient and more secure than the current models. © (2023). All Rights Reserved.

关键词： Authorization

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Spatial fuzzy C-means clustering and deep belief network for change detection in synthetic aperture radar images 5

Spatial fuzzy C-means clustering and deep belief network for...

引用

IET International Radar Conference 2020, IET IRC 2020

作者： Qi, Wenwen Wu, Lin Guo, Zhengwei Huang, Dan College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng475004 China College of Environment and Planning Henan University Kaifeng475004 China Department of Laboratory and Equipment Management Henan University Kaifeng475004 China

ISBN: (纸本)9781839535635

In this study, spatial fuzzy c-means (SFCM) clustering and deep belief network (DBN) method is presented for change detection in SAR images. There are three primary steps of this approach, they are given as follows: 1) Difference Image (DI) is firstly obtained by calculating the similarity of gray levels between multitemporal SAR images. 2) SFCM clustering is used to analyze DI, which combines the spatial information factor and the fuzzy cluster membership function, thus can effectively eliminate the interference of speckle noise. 3) DBN, a powerful data processing methodology that integrates deep learning techniques to extract underlying features and structures of complicated data, is trained to classify pixels to be changed or unchanged. The effectiveness and robustness of the proposed method are verified by experimental results with the Ottawa dataset. © 2020 Institution of engineering and technology. All rights reserved.

关键词： Membership functions

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MalScan: Android Malware Detection Based on Social-Network Centrality Analysis

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Yueming Suo, Wenqi Feng, Siyue Zou, Deqing Yang, Wei Liu, Yang Jin, Hai Huazhong University of Science and Technology 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 Wuhan430074 China Jinyinhu Laboratory Wuhan430074 China University of Texas at Dallas United States Nanyang Technological University Singapore Huazhong University of Science and Technology 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 Wuhan430074 China

Malware scanning of an app market is expected to be scalable and effective. However, existing approaches use syntax-based features that can be evaded by transformation attacks or semantic-based features which are usually extracted by expensive program analysis. Therefore, to address the scalability challenges of traditional heavyweight static analysis, we propose a graph-based lightweight approach MalScan for Android malware detection. MalScan considers the function call graph as a complex social network and employs centrality analysis on sensitive application program interfaces (APIs) to express the semantic characteristics of the graph. On this basis, machine learning algorithms and ensemble learning algorithms are applied to classify the extracted features. We evaluate MalScan on datasets of 104,892 benign apps and 108,640 malwares, and the results of experiments indicate that MalScan outperforms six state-of-the-art detectors and can quickly detect Android malware with an f-value as high as 99%. In addition, there are also significant improvements in the robustness of Android app evolution and robustness to obfuscation. Finally, we conduct an exhaustive statistical study of over one million applications in the Google-Play app market and successfully identify 498 zero-day malware, which further validates the feasibility of MalScan on market-wide malware scanning. © 2004-2012 IEEE.

关键词： Android malware

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Generative Federated Learning with Small and Large Models In Consumer Electronics for Privacy preserving data Fusion in Healthcare Internet of Things

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Ghazal, Taher M. Islam, Shayla Hasan, Mohammad Kamrul Abu-Shareha, Ahmad A. Mokhtar, Umi A. Khan, M. Attique Baili, Jamel Saeed, Ali Q. Bhattt, Mohammed Wasim Ahmad, Munir Al-Ahliyya Amman University Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia (UKM) Department of Networks and Cybersecurity Hourani Center for Applied Scientific Research Amman Jordan UCSI University Institute of Computer Science and Digital Innovation Kuala Lumpur 56000 Malaysia Universiti Kebangsaan Malaysia (UKM) Center for Cyber Security Faculty of Information Science and Technology Malaysia Al-Ahliyya Amman University Hourani Center for Applied Scientific Research Department of Data Science and Artificial Intelligence Amman Jordan Prince Mohammad bin Fahd University Saudi Arabia King Khalid University College of Computer Science Department of Computer Engineering Abha 61413 Saudi Arabia Northern Technical University Computer Center Ninevah Iraq Korea University College of Informatics Seoul 02841 South Korea

Healthcare Internet of Things (HIoT) requires large-scale privacy features to ensure maximum security in sharing sensitive physiological data in consumer electronics. Recent approaches utilize the fusion concept to provide maximum privacy in health data sharing. Embedded signing data fusion with the health observed data ensures privacy preserved sharing across heterogeneous medical consumer devices for diagnosis. This article proposes a Dependency-correlated data Fusion Scheme (DcDFS) to maximize the privacy of the health data-sharing process. The proposed scheme prepares separate key signing procedures using triple-DES (data encryption standard) to embed with the accumulated health data. The fusion process is carried out by defining key headers and integrity footers for authentication and verification. Therefore, the fusion generates a combined sequence of linear authentication and validation procedures enclosing the health data. In this scheme, the role of federated learning is to prevent permuted sequences for the same health data. This research integrates Small Language Model (SLM) and Large Language Model (LLM) into the federated learning module to support secure, scalable, and intelligent healthcare data sharing. Their collaboration enhances context-aware training while preserving privacy across decentralized, encrypted environments. A similar sequence mapped between the header and footer is responsible for discarding unauthorized data handling. The learning process verifies the permutation for many-to-one header to footer and vice versa. Therefore, the proposed fusion scheme generates a linear dependency between the actual and security-related data for maximum privacy. The proposed scheme achieves the following: the computation time is confined by 12.424%, the privacy leakage by 12.923%, and the computation efficiency is improved by 11.46%, as observed under the maximum sequences. © 1975-2011 IEEE.

关键词： data Fusion Federated Learning Healthcare IoT Privacy-Preserving Triple DES

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Adaptive Proposal Generation Network for temporal sentence localization in videos

arXiv

引用

arXiv 2021年

作者： Liu, Daizong Qu, Xiaoye Dong, Jianfeng Zhou, Pan The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Electronic Information and Communication Huazhong University of Science and Technology Huawei Cloud Zhejiang Gongshang University

We address the problem of temporal sentence localization in videos (TSLV). Traditional methods follow a top-down framework which localizes the target segment with predefined segment proposals. Although they have achieved decent performance, the proposals are handcrafted and redundant. Recently, bottom-up framework attracts increasing attention due to its superior efficiency. It directly predicts the probabilities for each frame as a boundary. However, the performance of bottom-up model is inferior to the top-down counterpart as it fails to exploit the segment-level interaction. In this paper, we propose an Adaptive Proposal Generation Network (APGN) to maintain the segment-level interaction while speeding up the efficiency. Specifically, we first perform a foreground-background classification upon the video and regress on the foreground frames to adaptively generate proposals. In this way, the handcrafted proposal design is discarded and the redundant proposals are decreased. Then, a proposal consolidation module is further developed to enhance the semantic of the generated proposals. Finally, we locate the target moments with these generated proposals following the top-down framework. Extensive experiments on three challenging benchmarks show that our proposed APGN significantly outperforms previous state-of-the-art methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Efficiency

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Coarse to fine: Domain adaptive crowd counting via adversarial scoring network

arXiv

引用

arXiv 2021年

作者： Zou, Zhikang Qu, Xiaoye Zhou, Pan Xu, Shuangjie Ye, Xiaoqing Wu, Wenhao Ye, Jin The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Baidu Inc. China Department of Computer Science and Engineering Hong Kong University of Science and Technology China

Recent deep networks have convincingly demonstrated high capability in crowd counting, which is a critical task attracting widespread attention due to its various industrial applications. Despite such progress, trained data-dependent models usually can not generalize well to unseen scenarios because of the inherent domain shift. To facilitate this issue, this paper proposes a novel adversarial scoring network (ASNet) to gradually bridge the gap across domains from coarse to fine granularity. In specific, at the coarse-grained stage, we design a dual-discriminator strategy to adapt source domain to be close to the targets from the perspectives of both global and local feature space via adversarial learning. The distributions between two domains can thus be aligned roughly. At the fine-grained stage, we explore the transferability of source characteristics by scoring how similar the source samples are to target ones from multiple levels based on generative probability derived from coarse stage. Guided by these hierarchical scores, the transferable source features are properly selected to enhance the knowledge transfer during the adaptation process. With the coarse-to-fine design, the generalization bottleneck induced from the domain discrepancy can be effectively alleviated. Three sets of migration experiments show that the proposed methods achieve state-of-the-art counting performance compared with major unsupervised methods. © 2021, CC BY.

关键词： Knowledge management

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