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Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

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Computers, Materials & Continua 2024年第5期79卷 2625-2646页

作者： Mingliang Sun Jie Yuan Xiaoyong Li Dongxiao Liu Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing100876China

Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.

关键词： Image encryption chaotic system compressive sensing arnold transform

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Game-theoretic private blockchain design in edge computing networks

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Digital Communications and Networks 2024年第6期10卷 1622-1634页

作者： Daoqi Han Yang Liu Fangwei Zhang Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and Telecommunications(BUPT)Beijing 100876China Beijing Institute of Astronautical Systems Engineering BeijingChina Beijing Institute of Technology BeijingChina

Considering the privacy challenges of secure storage and controlled flow,there is an urgent need to realize a decentralized ecosystem of private blockchain for cyberspace.A collaboration dilemma arises when the participants are self-interested and lack feedback of complete *** blockchains have similar faults,such as trustlessness,single-factor consensus,and heavily distributed ledger,preventing them from adapting to the heterogeneous and resource-constrained Internet of *** this paper,we develop the game-theoretic design of a two-sided rating with complete information feedback to stimulate collaborations for private *** design consists of an evolution strategy of the decision-making network and a computing power network for continuously verifiable *** formulate the optimum rating and resource scheduling problems as two-stage iterative games between participants and *** theoretically prove that the Stackelberg equilibrium exists and the group evolution is ***,we propose a multi-stage evolution consensus with feedback on a block-accounting workload for metadata *** continuously validate a block,the metadata of the optimum rating,privacy,and proofs are extracted to store on a lightweight ***,to increase resource utilization,surplus computing power is scheduled flexibly to enhance security by ***,the evaluation results show the validity and efficiency of our model,thereby solving the collaboration dilemma in the private blockchain.

关键词： Ecosystem Edge computing Feedback Stackelberg game Private blockchain Scalable computing power

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Modification Method for Internal Network Flow Anomaly Detection Based on Multi-timescale Analysis 9

Modification Method for Internal Network Flow Anomaly Detect...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Zhang, Xinyu Ding, Junmei Lu, Yueming Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350391367

With the continuous development of information technology, security issues from internal networks are becoming more and more important. Many anomaly detection algorithms are designed to identify anomalies, but these algorithms usually do not care about temporal information, or only care about fine-grained temporal information. On the other hand, deep-learning-based algorithms must rely on complex neural networks to capture temporal information, which is difficult to deploy and maintain in actual internal network environments. Addressing the aforementioned problems, this paper proposes a multi-timescale analyzer using wavelet packet transform and semi-supervised learning, and a method that uses the analyzer to modify the original anomaly scores called Multi-timescale Modification (MTM) method. The method is validated using the network flow dataset CIC-IDS 2017 and a real-world dataset. Experimental evaluations show that using the proposed method can obtain better performance than the original algorithms. © 2024 IEEE.

关键词： Self-supervised learning

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An efficient confidentiality protection solution for pub/sub system

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Cybersecurity 2023年第4期6卷 14-25页

作者： Jinglei Pei Yuyang Shi Qingling Feng Ruisheng Shi Lina Lan Shui Yu Jinqiao Shi Zhaofeng Ma Beijing University of Posts and Telecommunications Beijing100876China Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijingChina School of Computer Science University of Technology SydneySydneyNSWAustralia

Publish/subscribe(pub/sub)systems are widely used in large-scale messaging systems due to their asynchronous and decoupled *** the population of pub/sub cloud services,the privacy protection problem of pub/sub systems has started to emerge,and events and subscriptions are exposed when executing event matching on untrustworthy cloud ***,as the number of subscriptions increases,the effectiveness of the previous confidentiality protection approaches declines *** this paper,we propose SBM(scalable blind matching),an effective confidentiality protection scheme for pub/sub *** the best of our knowledge,SBM is the first scheme that applies order-preserving encryption algorithm to protect the system’s confidentiality and ensure its *** this scheme,SBM-I is highly effective in subscription matching but is unable to achieve ideal security IND-OCPA,whereas SBM-II is suggested to ensure system security and SGX is used to reduce interaction and boost ciphertext matching *** experiment demonstrates that this method has better matching performance compared to others:the average matching time of SBM-I is 3–4 orders of magnitude faster than the matching algorithm MP and SGX-based algorithm SCBR when the number of subscriptions is 500,000,and the average matching time of SBM-II is 40 times faster than MP and 24 times than SCBR.

关键词： Pub/sub Confidentiality Privacy protection SGX Scalability

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A Novel Evolutionary Neural Network-based Approach for Online Bus Scheduling 21

A Novel Evolutionary Neural Network-based Approach for Onlin...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Tian, Wenyang Wang, Feiyang Zuo, Xingquan School of Computer Science Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350365221

Bus scheduling is vital to ensure service quality and save operational costs. Current studies typically consider the problem as an optimization problem and solve it using exact or heuristic approaches. In this paper, we consider the problem as a sequential decision-making problem and propose an Evolutionary Neural Network-based Approach (ENNA) for it. A neural network is used to make a decision at each departure time in the bus timetable. It selects which bus should depart at that specific time. By this means, a complete bus scheduling scheme is generated by a series of decisions made by the neural network. The structure and weights of the neural network are optimized by a genetic algorithm. To meet the real-world operational needs of bus duty types and improve bus utilization, we design a bus duty type transition rule that dynamically decides the duty type of buses. Experimental results demonstrate that the proposed ENNA outperforms manual scheduling schemes and can generate bus scheduling schemes with fewer buses. © 2024 IEEE.

关键词： Genetic algorithms

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Framework for understanding intention-unbreakable malware

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Science China(Information Sciences) 2023年第4期66卷 174-192页

作者： Tiantian JI Binxing FANG Xiang CUI Zhongru WANG Peng LIAO Shouyou SONG Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of Education Beijing University of Posts and Telecommunications Cyberspace Institute of Advanced Technology Guangzhou University Chinese Academy of Cyberspace Studies Beijing Dig Apis Technology Co. Ltd.

The anti-analysis technology of malware has always been the focus in the cyberspace security field. As malware analysis techniques evolve, malware writers continually employ sophisticated anti-reverse engineering techniques to defeat and evade state-of-the-art analyzers. Therefore, to prepare for unknown attacks, studying malware analysis techniques is insufficient. More importantly, we should study new antianalysis techniques for malware. This paper expands the concept of anti-analysis malware and defines a type of intention-unbreakable malware(IUM). To help defenders fully understand and establish a defense system against the new security threats, this paper systematically discusses IUM from the perspectives of threat discovery, threat modeling, attribute analysis, and threat assessment; in addition, this study also provides the Po C implementations of IUM and demonstrates how attackers can use this type of malware to evade advanced security analysis, that is, accurate identification of target(class) victims and intention concealment in reaching non-target(class) victims. Finally, this paper provides several mitigation directions for combating IUM.

关键词： malware unbreakable property modeling mechanism accurate identification intention concealment

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LRChain:data protection and sharing method of learning archives based on consortium blockchain

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The Journal of China Universities of Posts and Telecommunications 2024年第4期31卷 28-42页

作者： Lan Lina Gao Yuhan Shi Ruisheng Wu Fenfen School of Humanities Beijing University of Posts and TelecommunicationsBeijing 100876China School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsMinistry of EducationBeijing 100876China

Learning archives management in traditional systems faces challenges such as inadequate security,weak tamper resistance,and limited sharing *** address these issues,this paper proposes LRChain,a method based on consortium blockchain,for lifelong learning archives data protection and *** employs a combination of on-chain and off-chain cooperative storage using a consortium chain and inter planetary file system(IPFS)to enhance data security and *** also enables fine-grained verification of learning archives through selective disclosure principles,ensuring privacy protection of sensitive ***,an attributebased encryption(ABE)algorithm is utilized to establish authorized access control for learning archives,facilitating safe and trusted *** evaluations and security analyses demonstrate that this method exhibits decentralization,strong security,tamper resistance,and performs well,effectively meeting the requirements for secure sharing of learning archive data.

关键词： learning archives consortium blockchain collaborative storage secure sharing

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Reliability Measurement Of Vehicle Sensor Data Based On Historical Perception 9

Reliability Measurement Of Vehicle Sensor Data Based On Hist...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Guo, Chenqing Zuo, Jinxin Lu, Yueming Xie, Weixuan Beijing University of Posts and Telecommunications School of Cyberspace Security Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350391367

On-board sensors are utilized for monitoring and collecting a diverse range of data pertaining to the vehicle and its surroundings. However, in the field of Internet of Vehicles, security threats encountered by vehicle sensors often result in compromised accuracy of collected data. Therefore, it is crucial to investigate trusted measurement methods for vehicle sensor data. This paper proposes a reliable measurement approach for vehicle sensor data based on historical perception. The method initially achieves historical feature extraction through time correlation of single sensor sequence data and spatial correlation of multisensor data. Subsequently, a reliability measurement method is devised to quantify real-time data reliability by assessing the deviation from the theoretical expected value. Finally, historical data reliability is integrated with real-time data reliability to enhance accuracy of the overall reliability measurement process. This approach takes into consideration spatio-temporal correlation of vehicle sensor data and resolves issues stemming from unbalanced datasets that lead to inaccurate evaluation results. Experimental findings demonstrate an accuracy rate of 98.91% for the proposed method. © 2024 IEEE.

关键词： Spatio-temporal data

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SSDAEE: Semi-Supervised Data-Augmented Event Extraction for Transfer Learning 8

SSDAEE: Semi-Supervised Data-Augmented Event Extraction for ...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Shen, Yueyue Song, Feifan Wu, Jingchen Wu, Xu School of Cyberspace Security Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Economics and Finance Changchun Finance College Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Jilin China School of Computer Science Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350331035

Although the general event extraction model has achieved good results in the standard data sets, there is a gap in the extraction results in low-resource environments. The general event extraction model still has room for improvement in the performance of special fields. Therefore, this paper proposes a semi-supervised reading comprehension event extraction model adapted to transfer learning, based on improved UDA for data enhancement, called SSDAEE, which can increase the accuracy of reading comprehension event type recognition. SSDAEE has a good performance on the public opinion data set. It can not only enhance a small number of supervised data in low-resource fields with unsupervised data but also improve the generalization ability of the model. Experiments show that when only 5% unsupervised data is used, the F1 score of SSDAEE on the public opinion data set is increased by 9% compared with the fully supervised model, and by 2% compared with other semi-supervised models. © 2023 IEEE.

关键词： data augmentation event extraction semi-supervised transfer learning

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An Unsupervised Graph Embedding Method Based on Dynamic Graph Attention Networks and Infomax for Link Prediction 8

An Unsupervised Graph Embedding Method Based on Dynamic Grap...

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8th International Conference on Data Science in Cyberspace, DSC 2023

作者： Lu, Jingzhu Song, Feifan Wu, Jingchen Wu, Xu School of Cyberspace Security Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China School of Economics and Finance Changchun Finance College Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Jilin China School of Computer Science Beijing University of Posts and Telecommunications Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9798350331035

Due to the lack of labeled data in many real-world scenarios, Graph Neural Network models that require a large volume of labeled data are not effective, and research based on unsupervised learning frameworks has become essential. To solve the problem of sparsity in labeled graph data, this paper proposes an unsupervised graph representation learning method based on graph infomax and dynamic graph attention mechanism(IMDGA). This method conducts unsupervised learning based on the idea of contrastive learning and optimizes the encoder by calculating the maximum mutual information between the graph's local information and global information. At the same time, we use a dynamic Graph Attention Network as an encoder model to prevent the Graph Convolutional Neural network's inability to adapt to neighbor nodes with different weights. Finally, link prediction experiments on three public data sets of cora, citeseer, and pumbed show that the proposed model has achieved superior results compared with other graph representation learning, showing its effectiveness and competitiveness. © 2023 IEEE.

关键词： dynamic graph attention networks graph representation learning maximum mutual information unsupervised learning

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