检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Zero-Shot Detection of LLM-Generated Text using Token Cohesiveness

Zero-Shot Detection of LLM-Generated Text using Token Cohesi...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Shixuan Wang, Quan MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications China

ISBN: (纸本)9798891761643

The increasing capability and widespread usage of large language models (LLMs) highlight the desirability of automatic detection of LLM-generated text. Zero-shot detectors, due to their training-free nature, have received considerable attention and notable success. In this paper, we identify a new feature, token cohesiveness, that is useful for zero-shot detection, and we demonstrate that LLM-generated text tends to exhibit higher token cohesiveness than human-written text. Based on this observation, we devise TOCSIN, a generic dual-channel detection paradigm that uses token cohesiveness as a plug-and-play module to improve existing zero-shot detectors. To calculate token cohesiveness, TOCSIN only requires a few rounds of random token deletion and semantic difference measurement, making it particularly suitable for a practical black-box setting where the source model used for generation is not accessible. Extensive experiments with four state-of-the-art base detectors on various datasets, source models, and evaluation settings demonstrate the effectiveness and generality of the proposed approach. Code available at: https://***/Shixuan-Ma/TOCSIN. © 2024 Association for Computational Linguistics.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

DRLMS: a multipath scheduler based on deep reinforcement learning 3

DRLMS: a multipath scheduler based on deep reinforcement lea...

引用

3rd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2024

作者： Zhang, Mengyang Yuan, Kaiguo Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781510680098

Most current network devices have multiple network interfaces, and multipath transport protocols can utilize multiple network paths (e.g., WiFi and cellular) to improve the performance and reliability of network transmission. The scheduler of the multipath transmission protocol determines the path to which each data packet should be transmitted, and is a key module that affects multipath transmission. However, current multipath schedulers cannot adapt well to various user usage scenarios. In this paper, we propose DRLMS, a deep reinforcement learning based multipath scheduler. DRLMS uses deep reinforcement learning to train neural networks to generate packet scheduling policies. It optimizes the scheduling strategy through feedback to the neural network through the reward function based on the current user usage scenario and QoS. We implement DRLMS in the MPQUIC protocol and compared it with current multipath schedulers. The results show that DRLMS's adaptability to user usage scenarios is significantly outperforms other schedulers. © 2024 SPIE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

A Neural Network and Bloom Filter-Based Name Search Method in Named Data Networking 4

A Neural Network and Bloom Filter-Based Name Search Method i...

引用

4th International Conference on Neural Networks, Information and Communication Engineering, NNICE 2024

作者： Liu, Yue Li, Xiaoyong Si, Xiaotian Zhang, Mengyang Yuan, Kaiguo Li, Pengcheng Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350394375

In a new network architecture like NDN, designing an efficient name lookup method has always been a valuable challenge. The paper studies the name lookup technology based on the Bloom filter and BPNN neural network mapping model in named data networking. In the part of the hybrid model based on the Bloom filter and BPNN, the paper proposes the fusion idea of the Bloom filter and BPNN, and provides a detailed introduction to the construction process of the hybrid model. At the same time, in order to improve the memory utilization of traditional Bloom filters, this article proposes a way to use neural networks to enhance it, and proved its effectiveness through experiments. © 2024 IEEE.

关键词： Network architecture

来源：评论

学校读者我要写书评

暂无评论

A recommendation algorithm based on adaptive privacy protection 9

A recommendation algorithm based on adaptive privacy protect...

引用

9th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2024

作者： Song, Yiyang Li, Xiaoyong Yuan, Kaiguo Kong, Weiyu Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510683303

Differential privacy, as a provable privacy protection framework, has been widely applied in recommendation systems. However, the integration of existing collaborative filtering algorithms with differential privacy typically encounters two issues. Firstly, algorithms often use the same privacy budget for data from different time periods, introducing unnecessary noise and reducing performance. Secondly, when using gradient descent to solve collaborative filtering models, the allocation of privacy budgets is relatively simple, usually evenly distributed across each iteration. This allocation requires a predefined number of iterations. If the number is too small, the algorithm may terminate before reaching the optimal solution;if too large, each budget per iteration may be too small, leading to excessive noise. To address these issues, an adaptive privacy budget recommendation algorithm is proposed. Firstly, a dynamic time window-based privacy budget calculation algorithm is utilized to provide varying levels of privacy protection for rating data from different time periods, thus avoiding unnecessary noise. Then, an adaptive privacy budget gradient descent strategy is employed to solve the collaborative filtering model, which dynamically allocates privacy budgets based on the noise gradient of each round. This algorithm is compared with existing differential privacy-based recommendation algorithms on the Movielens-1M dataset, demonstrating superior predictive accuracy and lower sensitivity to privacy protection. © 2024 SPIE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

NFT Security Matrix:Towards Modeling NFT Ecosystem Threat

引用

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3255-3285页

作者： Peng Liao Chaoge Liu Jie Yin Zhi Wang Xiang Cui Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and TelecommunicationsBeijing100876China Zhongguancun Laboratory Beijing100094China Institute of Information Engineering Chinese Academy of SciencesBeijing100085China

Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens(NFTs).To be specific,the total trading volume of digital assets reached an astounding$55.5 billion in ***,numerous security concerns have been raised by the rapid expansion of the NFT *** holders are exposed to a plethora of scams and traps,putting their digital assets at risk of being ***,academic research on NFT security is scarce,and the security issues have aroused rare *** this study,the NFT ecological process is comprehensively *** process falls into five different stages encompassing the entire lifecycle of ***,the security issues regarding the respective stage are elaborated and analyzed in depth.A matrix model is proposed as a novel contribution to the categorization of NFT security *** data are collected from social networks,the Ethereum blockchain,and NFT markets to substantiate our claims regarding the severity of security concerns in the NFT *** this comprehensive dataset,nine key NFT security issues are identified from the matrix model and then subjected to qualitative and quantitative *** study aims to shed light on the severity of NFT ecosystem security *** findings stress the need for increased attention and proactive measures to safeguard the NFT ecosystem.

关键词： Non-fungible token blockchain cyber security

来源：评论

学校读者我要写书评

暂无评论

Game-theoretic private blockchain design in edge computing networks

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

Modification Method for Internal Network Flow Anomaly Detect...

引用

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

来源：评论

学校读者我要写书评

暂无评论

An IoT Device Recognition Method based on Convolutional Neural Network 3

An IoT Device Recognition Method based on Convolutional Neur...

引用

3rd International Conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Lu, Minghao Gao, Yali Li, Linghui Li, Xiaoyong Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798350335972

Device recognition is the primary step toward a secure IoT system. However, the existing equipment recognition technology often faces the problems of unobvious data characteristics and insufficient training samples, resulting in low recognition rate. To address this problem, a convolutional neural network-based IoT device recognition method is proposed. We first extract the background icons of various IoT devices through the Internet, and then use the ResNet50 neural network to extract icon feature vectors to build an IoT icon library, and realize accurate identification of device types through image retrieval. The experimental results show that the accuracy rate of sampling retrieval in the icon library can reach 98.5%, and the recognition accuracy rate outside the library can reach 83.3%, which can effectively identify the type of IoT devices. © 2023 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Encrypted Traffic Classification Model Based on SwinT-CNN 4

Encrypted Traffic Classification Model Based on SwinT-CNN

引用

4th International Conference on Computer Engineering and Application, ICCEA 2023

作者： Wang, Yueyang Gao, Yali Li, Xiaoyong Yuan, Jie Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350347548

With the rapid development of the network, the proportion of encrypted traffic in the network is increasing, which brings great challenges to traffic classification. The traditional encrypted traffic classification algorithm extracts the port number of the traffic or the load information of the traffic packet for feature matching, which has no effect in the current encrypted traffic. The encrypted traffic classification algorithm based on machine learning especially relies on the features extracted by professionals for encrypted traffic, which is inefficient and has low accuracy. And, The accuracy and overall performance of existing deep learning methods also need to be improved. Therefore, in order to improve the accuracy of encrypted traffic classification, this paper proposes an encrypted traffic classification method based on deep learning, which tries to combine the core modules of CNN and Swin Transformer with the encrypted traffic classification model to realize the identification of the application type of encrypted traffic. Among them, the CNN module is able to capture the local spatial information characteristics of encrypted traffic data. The multi-head attention mechanism of Swin Transformer can capture global information about the association between attributes of the data. The model is tested on the public ISCX VPN-nonVPN encryption dataset to evaluate the performance of the proposed model. Experimental results show that the accuracy of our proposed model reaches 96.7%. Compared with the other best-performing deep learning models, the proposed model improves the accuracy by 3.1% and the F1 value by 2.7%. © 2023 IEEE.

关键词： Cryptography

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：