检索结果-内蒙古大学图书馆

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

An Intrusion Detection Method Based on Transformer-LSTM Model 3

An Intrusion Detection Method Based on Transformer-LSTM Mode...

引用

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

作者： Zhang, Zhipeng Si, Xiaotian Li, Linghui Gao, Yali Li, Xiaoyong Yuan, Jie Xing, Guoqiang Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350335972

With the development of network technologies, network intrusion has become increasing complex which makes the intrusion detection challenging. Traditional intrusion detection algorithms detect intrusion traffic through intrusion traffic characteristics or machine learning. These methods are inefficient due to the dependence of manual work. Therefore, in order to improve the efficiency and the accuracy, we propose an intrusion detection method based on deep learning. We integrate the Transformer and LSTM module with intrusion detection model to automatically detect network intrusion. The Transformer and LSTM can capture the temporal information of the traffic data which benefits to distinguish the abnormal data from normal data. We conduct experiments on the publicly available NSL-KDD dataset to evaluate the performance of our proposed model. The experimental results show that the proposed model outperforms other deep learning based models. © 2023 IEEE.

关键词： Intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Browser Fingerprint Linking Based on Transformer-encoder Model

Browser Fingerprint Linking Based on Transformer-encoder Mod...

引用

2023 International Conference on Computer Network Security and Software Engineering, CNSSE 2023

作者： Tan, Yun Li, Xiaoyong Si, Xiaotian Li, Linghui Gao, Yali Yuan, Jie Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (数字)9781510666528

ISBN: (纸本)9781510666511

Browser fingerprinting has been used as a user-tracking technique in recent years. As a long-term tracking technique, it requires not only obtaining unique browser fingerprints but also linking fingerprints from the same browser instance in that browsers change rapidly and frequently. To improve the efficiency of linking the evolving browser fingerprints, in this paper, we propose a browser fingerprint linking method based on Transformer-encoder. Transformer-encoder utilizes an attention mechanism to focus on certain parts of the input sequence, enabling it to capture complex connections and interactions within the data more efficiently. To make the most of the parallel processing mechanism of the Transformer-encoder, we combine multiple fingerprint comparison vectors into an input vector to train the model. We conduct extensive experiments on a public dataset to evaluate our proposed model. The experimental results show that our model outperforms some existing models, which proves the effectiveness of the Transformer-encoder in linking browser fingerprints. © 2023 SPIE.

关键词： Tracking (position)

来源：评论

学校读者我要写书评

暂无评论

PAUP: a pre-awareness insider threat detection method based on user psychoanalysis 9

PAUP: a pre-awareness insider threat detection method based ...

引用

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

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

ISBN: (纸本)9781510683303

Internal personnel within an organization often have privileged access to critical systems and sensitive information. They are familiar with the internal network structure, business processes, and security measures, which can lead to insider threats that are more insidious, long-term, destructive and diverse, posing a serious threat to enterprises and organizations. However, existing models for detecting insider threats primarily focus on modeling user behavior information but seldom take into account the valuable information from the psychological personality of insider personnel for threat detection. To address this limitation and better analyze the impact of user attributes on insider threats, a new direction for insider threat user analysis is proposed. This involves analyzing and visualizing the relationship between users' personalities and the execution of insider threat behaviors, using data analysis. Additionally, a decision tree model is constructed to realize insider threat detection based on user psychology, using feature_importance, a relative importance metric of features generated in the decision tree decision-making process, to judge the importance of different personality traits for insider threat detection. To further enhance the detection process, an insider threat user clustering method based on Fuzzy C-Means clustering is realized, and the groups are divided according to the user's psychological assessment scores to realize the early perceived localization of risky users. These approaches provide new ideas for finding new research directions in the field of insider threat. © 2024 SPIE.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

CMI-AIGCX at GenAI Detection Task 2: Leveraging Multilingual Proxy LLMs for Machine-Generated Text Detection in Academic Essays 1

CMI-AIGCX at GenAI Detection Task 2: Leveraging Multilingual...

引用

1st Workshop on GenAI Content Detection, GenAIDetect 2025

作者： Jiao, Kaijie Yao, Xingyu Ma, Shixuan Fang, Sifan Guo, Zikang Xu, Benfeng Zhang, Licheng Wang, Quan Zhang, Yongdong Mao, Zhendong University of Science and Technology of China Hefei China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9798891762053

This paper presents the approach we proposed for GenAI Detection Task 2, which aims to classify a given text as either machine-generated or human-written, with a particular emphasis on academic essays. We participated in subtasks A and B, which focus on detecting English and Arabic essays, respectively. We propose a simple and efficient method for detecting machine-generated essays, where we use the Llama-3.18B as a proxy to capture the essence of each token in the text. These essences are processed and classified using a refined feature classification network. Our approach does not require fine-tuning the LLM. Instead, we leverage its extensive multilingual knowledge acquired during pretraining to significantly enhance detection performance. The results validate the effectiveness of our approach and demonstrate that leveraging a proxy model with diverse multilingual knowledge can significantly enhance the detection of machine-generated text across multiple languages, regardless of model size. In Subtask A, we achieved an F1 score of 99.9%, ranking first out of 26 teams. In Subtask B, we achieved an F1 score of 96.5%, placing fourth out of 22 teams, with the same score as the third-place team. © 2025 International Conference on Computational Linguistics.

关键词： Knowledge acquisition

来源：评论

学校读者我要写书评

暂无评论

IDEATE: Detecting AI-Generated Text using Internal and External Factual Structures 30

IDEATE: Detecting AI-Generated Text using Internal and Exter...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wang, Quan Zhang, Licheng Guo, Zikang Mao, Zhendong MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China University of Science and Technology of China Hefei China

ISBN: (纸本)9782493814104

The effective detection of AI-generated text is a vital principle to ensure responsible use of large language models (LLMs). Previous studies mainly focused on discovering and utilizing internal evidences contained in the text itself to perform the detection, while ignoring external evidences implicated in an established knowledge graph (KG) which may also be key discriminative factors between AI-generated and human-written text. To address this deficiency, we propose IDEATE, a novel hierarchical graph network that utilizes both internal and external factual structures to detect AI-generated text. IDEATE consists of a mention-level subgraph at the bottom to describe internal factual structures of mentioned entities reflected in the input text, and an entity-level subgraph at the top to describe external factual structures of mentioned entities reflected in an external KG. Hierarchical graph convolution is then applied successively on the two subgraphs, through which the two types of factual structures will be embedded into the output and used for the final detection. Extensive experiments on four benchmarking datasets show that IDEATE consistently outperforms current state-of-the-art methods in detecting text generated by various LLMs, ranging from GPT-2 to the more powerful ChatGPT, verifying the necessity and superiority of introducing external evidences for AI-generated text detection. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Knowledge graph

来源：评论

学校读者我要写书评

暂无评论

An efficient confidentiality protection solution for pub/sub system

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Framework for understanding intention-unbreakable malware

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：