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arXiv 2024年

作者： Tang, Datao Cao, Xiangyong Hou, Xingsong Jiang, Zhongyuan Liu, Junmin Meng, Deyu The School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Information and Communications Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710049 China The School of Mathematics and Statistics the Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://***/Sonettoo/CRS-Diff. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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A Feature-Based Detection System of Adversarial Sample Attack 3rd

A Feature-Based Detection System of Adversarial Sample Attac...

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Wang, Fangwei Lu, Yuanyuan Li, Qingru Wang, Changguang Lab of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (纸本)9783030624590

With the increase of malware, traditional malicious detection methods are not suitable to high-intensity detection work. In response to this question, many malware detection methods based on Machine Learning (ML-based) are proposed to address this problem. However, the ML-based detection method is vulnerable to the attack from adversarial samples. To overcome the limitation, we present our model. We protect the classification model from adversarial example attack by quantifying the similarity between the extracted image features and the expected features of the prediction class. Experimental results demonstrate that our model can detect the misclassification caused by adversarial samples with a higher accuracy than that of the Resnet-50. © 2020, Springer Nature Switzerland AG.

关键词： Data visualization

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Improving Complex Knowledge Base Question Answering with Relation-Aware Subgraph Retrieval and Reasoning network

Improving Complex Knowledge Base Question Answering with Rel...

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International Joint Conference on Neural networks (IJCNN)

作者： Dan Luo Jiawei Sheng Hongbo Xu Lihong Wang Bin Wang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Xiaomi AI Lab Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

Complex Knowledge Base Question Answering aims to answer a complex question over a Knowledge Base. A mainstream solution is based on information retrieval, which usually extracts a pivotal subgraph from entire Knowledge Base to locate candidate answers, and then determines the plausible answers with semantic matching between candidate answers and the question. However, such a paradigm can have two critical problems: 1) Complex Knowledge Base Question Answering can be sensitive to the subgraph, since a small subgraph may exclude the answers, while a large one may introduce a lot of noise; 2) directly deriving answers with semantic matching neglects the global topology in the Knowledge Base, which may limit the capability in answer reasoning. To tackle above challenges, we propose the Relation-Aware Subgraph Retrieval and Reasoning network, where relations are emphasized to construct subgraphs and answer reasoning. Specifically, we present a Relation-Aware Subgraph Retrieval (RASR) method to initialize and prune subgraphs with the guidance of relation semantics. To compre-hensively understand the complex correlations between the question and candidate answers, we put forward a Relation-Aware Reasoning network (RARN), which contains a text reasoning module focusing on the semantics understanding of the question and a graph reasoning module focusing on mining the topology between the topic entities and the answers. Experiments on two classical benchmark datasets show that our reasoning model outperforms the state-of-the-art results of information Retrieval models. What's more, data statistical analysis on the subgraphs demonstrates the effectiveness of our proposed subgraph retrieval method.

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A Novel Intrusion Detection System for Malware Based on Time-Series Meta-learning 1

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3rd International Conference on Machine Learning for Cyber security, ML4CS 2020

作者： Wang, Fangwei Yang, Shaojie Wang, Changguang Li, Qingru Lab of Network and Information Security of Hebei Province Hebei Normal University Shijiazhuang050024 China College of Computer and Cyber Security Hebei Normal University Shijiazhuang050024 China

ISBN: (数字)9783030622237

ISBN: (纸本)9783030622220

In recent years, frequent occurrence of network security incidents indicates that host security is more and more fragile. However, current protection tools leads to reduce the efficiency of CPU or GPU. Meanwhile, they give up active defense and increase the security risk. Unfortunately, the existing intrusion detection systems seldom adjust the defense policy according to the host’s performance and the time when the attack might occur. Thus, different from traditional intrusion detection systems, our system is capable of intelligently detecting and predicting threats. Firstly, our system converts the malware into gray-scale images according to the instruction execution logic. Secondly, the system uses a computer vision method to identify the signature of the gray-scale images. Finally, the proposed system classifies malware family. Specifically, the system can also predict the time when a host faces a severe threat using time-series datasets and create a multi-neural network task for defending the threat. Then, a meta-learning framework is utilized to improve malware detection accuracy and defend against attacks effectively. The experimental results show that our system can accurately classify 15 malware families, and we compare our detection results with that of other IDSs, which proves that our system achieves a better performance. © 2020, Springer Nature Switzerland AG.

关键词： Malware

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Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment

arXiv

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arXiv 2023年

作者： Li, Qian Guo, Shu Luo, Yangyifei Ji, Cheng Wang, Lihong Sheng, Jiawei Li, Jianxin School of Computer Science and Engineering BDBC Beihang University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security UCAS Beijing China SCSE Beihang University Zhongguancun Lab Beijing China

The multi-modal entity alignment (MMEA) aims to find all equivalent entity pairs between multi-modal knowledge graphs (MMKGs). Rich attributes and neighboring entities are valuable for the alignment task, but existing works ignore contextual gap problems that the aligned entities have different numbers of attributes on specific modality when learning entity representations. In this paper, we propose a novel attribute-consistent knowledge graph representation learning framework for MMEA (ACK-MMEA) to compensate the contextual gaps through incorporating consistent alignment knowledge. Attribute-consistent KGs (ACKGs) are first constructed via multi-modal attribute uniformization with merge and generate operators so that each entity has one and only one uniform feature in each modality. The ACKGs are then fed into a relation-aware graph neural network with random dropouts, to obtain aggregated relation representations and robust entity representations. In order to evaluate the ACK-MMEA facilitated for entity alignment, we specially design a joint alignment loss for both entity and attribute evaluation. Extensive experiments conducted on two benchmark datasets show that our approach achieves excellent performance compared to its competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Knowledge graph

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VFGCN: A Vertical Federated Learning Framework With Privacy Preserving for Graph Convolutional network

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wang, Gang Li, Qingming Liu, Ximeng Yan, Xiaoran Dong, Qingkuan Wu, Huiwen Kong, Xiangjie Zhou, Li Zhejiang Lab HangZhou310000 China Zhejiang University College of Computer Science and Technology Hangzhou310058 China Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Big Data Fuzhou350108 China Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Zhejiang University of Technology College of Computer Science and Technology Hangzhou310023 China Dalian University of Technology School of Software Dalian116620 China

Due to the robust representational capabilities of graph data, employing graph neural networks for its processing has demonstrated superior performance over conventional deep learning algorithms. Graph data encompasses abundant features and structural information;however, its large-scale collection is often challenging in practice. This difficulty arises because data predominantly exists in isolated compartments, making it arduous to harmonize information across various organizations or to enable multiple organizations to collaborate effectively while safeguarding local data privacy. In light of an extreme data distribution scenario, where each client possesses distinct nodes with partially overlapping segments yet divergent data features, we introduce a dual-cloud server architecture. This framework encompasses the design of four secure subprotocols: ReEnc (secure re-encryption), SecPSI (secure outsourcing of PSI), SecWeight (secure weight calculation), and SecAgg (secure aggregation). Together, these components facilitate a vertical federated learning framework for graph convolutional networks, ensuring privacy preservation. We provide a security proof for the entire system and extensive evaluation on three benchmark datasets (Cora, Citeseer, and Pubmed) illustrates that our Vertical Federated Graph Convolutional network (VFGCN) surpasses existing privacy-preserving methodologies. © 2004-2012 IEEE.

关键词： Privacy by design

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Dynamic Pseudonym Semantic-location Privacy Protection Based on Continuous Query for Road network

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International Journal of network security 2021年第4期23卷 642-649页

作者： Wang, Yonglu Zuo, Kaizhong Liu, Rui Zhao, Jun School of Computer and Information Anhui Normal University Anhui Provincial Key Lab of Network and Information Security Anhui Normal University Wuhu241002 China

Continuous queries are one of the most common queries in location-based services, although particularly useful, such queries raise serious privacy concerns. Due to frequently updated continuous locations, continuous query privacy will be disclosed. Constructing a constant anonymous user set is one of the main methods used to protect privacy information. The exiting methods don’t consider query duration or users’ personalized privacy requirements, and the semantic information of location isn’t fully taken into account. In order to solve these problems, a dynamic pseudonym semantic-location privacy protection algorithm based on continuous query for road network is proposed in this paper. Firstly, the city road network in question is divided using the Voronoi diagram. Secondly, a secure anonymous region is generated by adopting the greedy strategy based on location and privacy requirement. Finally, the intersection of anonymous user sets at different times is calculated, and the user’s identity information is updated using the dynamic pseudonym mechanism. Experimental results show that the proposed method can satisfy users’ personalized privacy requirements and improve the security of privacy protection and query efficiency. © 2021

关键词： Location based services

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Joint Space-Time Sparsity Based Jamming Detection for Mission-Critical mMTC networks

arXiv

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arXiv 2023年

作者： Wang, Shao-Di Wang, Hui-Ming Li, Zhetao Leung, Victor C.M. The School of Information and Communication Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'An710049 China The Key Laboratory of Hunnan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent Network School of Computer Science Xiangtan University Xiangtan411105 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada

For mission-critical massive machine-type communications (mMTC) applications, the messages are required to be delivered in real-time. However, due to the weak security protection capabilities of the low-cost and low-complexity machine-type devices, active jamming attack in the uplink access is a serious threat. Uplink access jamming (UAJ) can increase the number of dropped/retransmitted packets and restrict or prevent the normal device access. To tackle this vital and challenging problem, we propose a novel UAJ detection method based on the joint space-time sparsity (JSTS). Our key insight is that the JSTS-based feature will be significantly impacted if UAJ happens, since only a small fraction of the devices are active and the traffic pattern for each device is sporadic in the normal state. Unlike the existing detection methods under batch mode (i.e., all sample observations are collected before making a decision), the JSTS-based detection is performed in a sequential manner by processing the received signals one by one, which can detect UAJ as quickly as possible. Moreover, the proposed JSTS-based method does not rely on the prior knowledge of the attackers, since it only cares the abrupt change in the JSTS-based feature on each frame. Numerical results evaluate and confirm the effectiveness of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Numerical methods

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TPM, a pattern for an architecture for trusted computing 20

TPM, a pattern for an architecture for trusted computing

引用

2020 European Conference on Pattern Languages of Programs, EuroPLoP 2020

作者： Muñoz, Antonio Fernandez, Eduardo B. Network Information and Computer Security Lab University of Malaga Malaga Spain Dept. of Computer and Electrical Eng. and Computer Science Florida Atlantic University Boca RatonFL United States

ISBN: (纸本)9781450377690

Trusted Computing (TC) is a valuable technology to use when we need to process highly sensitive data and several variants of it are now available. We present here a pattern that describes the architecture of a standard for TC, the Trusted Platform Module (TPM), which is an architecture for which there are several possible implementations. Standards are effectively patterns because of the freedom to implement them in a variety of ways. The TPM architecture contains cryptographic functions to attest the use of legitimate hardware and software platforms and defines operations to perform related trusted operations. Some of its variants are intended to only store private keys in a secure way. Although promising, this technology often has not been properly used. This motivated us for defining a comprehensive methodology that facilitates the design and development of secure TC-equipped systems that can fully exploit the functionalities that this technology provides. This paper provides the first step of this methodology in the form of a security pattern. We have tried to provide a reasonable amount of information in our pattern description, aimed at the needs of application designers. This pattern is described in an abstract form independent of implementation details, but expressed with enough detail and precision for the use of designers. © 2020 ACM.

关键词： Trusted computing

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BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

BEIR:A Blockchain-based Encrypted Image Retrieval Scheme

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International Conference on computer Supported Cooperative Work in Design

作者： Xianxian Li Jie Li Feng Yu Xuemei Fu JunHao Yang Yue Chen Guangxi Key Lab of Multi-source Information Mining & Security Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781728165974;9781728165981

Encrypted image retrieval may return incorrect or incomplete results due to threats from malicious cloud servers. Most of the existing solutions focus on the efficiency and accuracy of retrieval, lack of verification of the completeness of search results, to achieve the reliability of search results and the transparency of the search process, we explore characteristic such as the decentralization and tamper-proof of blockchain, proposed a blockchain-based encrypted image retrieval scheme. This scheme stores the encrypted index on the blockchain (Ethereum), through the blockchain consensus mechanism and the function of searching on the smart contract, ensures the integrity and correctness of search results, then outsources the corresponding encrypted images to the cloud server to reduce storage cost, and designs a double-layer index structure using the bag of visual word model and simhash in the process of image similarity index. Experiments show that the reliability, high retrieval efficiency, and precision of the scheme also have a good privacy protection effect.

关键词： Privacy Visualization Image retrieval Smart contracts Blockchain Search problems Encryption

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