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IEEE Conference on Wireless Communications and Networking

作者： Yueqin Ge Xiaoyong Li Binsi Cai Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

The rapid development of the Internet and the increasingly complex structure of network space make the network security situation more and more serious. Abnormal traffic detection is an important part of network intrusion detection and plays an important role in the field of network security. Traditional machine learning methods rely too much on feature selection and extraction and have high false positive rate and delay caused by abnormal behavior recognition. Deep learning models such as CNN and RNN have many parameters and long training time, which seriously affect the early warning function of network intrusion detection. Aiming at the problem that the parameter redundancy of deep learning models limits the corresponding model deployment in some scenarios and devices, this paper proposes a lightweight bit-operation abnormal traffic detection method based on XNOR-net convolutional neural network (XNOR-CNN). The model uses convolutional neural network(CNN) and long-short term memory(LSTM) to comprehensively analyze the temporal and spatial characteristics of network traffic, and predicts and classifies the attack behaviors in the future network traffic. In particular, the model transforms the complex convolution process into bit operation between vectors by XNOR operation, which reduces the storage of weight vectors and complex redundant calculation, greatly improves the speed of network training and reduces the consumption of network memory. In this paper, the accuracy and efficiency of XNORCNN model are proved through a large number of comparative experiments on real traffic datasets.

关键词： Deep learning Training Network intrusion detection Telecommunication traffic Predictive models Network security Feature extraction

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AutoGuard: A Secure Implementation of the Conditional Branch Instruction

AutoGuard: A Secure Implementation of the Conditional Branch...

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IEEE Asian Hardware-Oriented Security and Trust (AsianHOST)

作者： Zeru Lan Chunlu Wang Pengfei Qiu Yu Jin Yihao Yang Dongsheng Wang Gang Qu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Zhongguancun Laboratory Tsinghua University University of Maryland College Park

ISBN: (数字)9798350368062

ISBN: (纸本)9798350368079

Side-channels are one of the major security threats to processors, which use the unintentional leakage of time, power consumption, and electromagnetic radiation from computers as a basis for obtaining important secret data in the computers. The Branch Target Buffer (BTB) and Branch History Buffer (BHB) side-channels are the recently discovered side-channel vulnerabilities that can be utilized to implement many high-threat hardware vulnerability attacks like Spectre and BranchScope. However, although there are many effective defense methods for BTB-based and BHB-based side-channel attacks, the relative isolation of each method from each other seriously affects the processor's performance and also increases resource consumption. In this paper, we propose a secure conditional branching architecture (we call it AutoGuard), which is a novel mitigation mechanism for BTB-based and BHB-based Side Channel Attacks (SCAs). Meanwhile, AutoGuard is also capable of effectively mitigating Spectre attacks (including variant 1 and variant 2). AutoGuard first ensures that only the correct addresses can be executed during the conditional branch by address blocking and adding partial instruction dependencies. Subsequently, AutoGuard modifies the microarchitectural execution process of the partial conditional branch to safely execute instructions with the assistance of the Return Stack Buffer (RSB). Finally, AutoGuard determines the final instruction to be executed by determining the instruction address at the top of the RSB stack and the top of the execution stack. Through the above process, we design a security gadget in AutoGuard that can automatically identify and modify conditional branches to improve their security. We successfully deployed AutoGuard on multiple processors and Linux kernels. Through experimental evaluation, we find that AutoGuard consumes fewer resources and has a lesser performance impact than other existing BTB-based and BHB-based side-channel attack protection

关键词： Computers Program processors Power demand Prevention and mitigation Side-channel attacks Hardware Security Protection Transient analysis Optimization

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A deepfake video detection method based on multi-modal deep learning method 2

A deepfake video detection method based on multi-modal deep ...

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2nd International Conference on Electronics, Communications and Information Technology, CECIT 2021

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

ISBN: (纸本)9781665437578

Recently, most deepfake video classification tasks depend on frame-level features and try to train deep neural networks to characterize fake videos. Although this kind of methods can achieve good results, they also waste the audio information and timing information of the deepfake video dataset. Therefore, to make better use of the audio information, we propose a multi-modal method to detect deepfake videos. The principle of our method is based on the mismatch between audio information and visual information, such as the inconsistency of mouth shape and voice. We calculate the modality dissonance score(MDS score) of videos to classify true/false videos. Extensive experiments reach 84.4% accuracy on the DFDC dataset, which demonstrates the effectiveness of our method. © 2021 IEEE.

关键词： Deep neural networks

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Violence Detection In Videos via Motion-Guided Global and Local Views 8

Violence Detection In Videos via Motion-Guided Global and Lo...

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

作者： Su, Ning Sun, Lijuan Gao, Yutong Wu, Jingchen Wu, Xu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security Bupt China School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing China Bupt China

ISBN: (纸本)9798350331035

Video violence detection aims to locate the time window in which violent behavior occurs. Most methods focus on utilizing RGB features directly or only fusing RGB and audio features, ignoring the effective exploitation of motion information carried in optical flow. This lack of emphasis on motion information may impact the overall accuracy of violence detection. Moreover, we observe that videos contain strong local correlations, so it is insufficient to analyze only from a holistic perspective without capturing finer details. Therefore, in this paper, we design a novel Global-and-Local Cross-Modal Network (GL-CMN) for violence detection, which effectively integrates motion information and multi-granularity features from target videos. Specifically, we first propose a Motion-Guided Attention Module (MGAM) to obtain enhanced visual features by calibrating RGB features through optical flow features. Secondly, The enhanced features are simultaneously fed into two parallel branches of the network. The global branch fuses the visual and audio features into holistic representations. The local branch extracts multi-scale temporal dependencies through dilated convolutions. Experiments demonstrate that our method exhibits significant improvement compared to previous state-of-the-art methods on the XD-Violence dataset. © 2023 IEEE.

关键词： Attention Mechanism Multimodality Multiple Instance Learning Violence Detection

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EFM: A Negative Network Public Opinion Early Warning Model Based on Evolutionary Feature Mining 8

EFM: A Negative Network Public Opinion Early Warning Model B...

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

作者： Meng, Fanfeixue Wu, Jingchen Song, Feifan Wu, Xu Hu, Lan Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Cyberspace Security Bupt China Beijing University of Posts and Telecommunications Beijing China Bupt China School of Economics and Finance Changchun Finance College China

ISBN: (纸本)9798350331035

With the popularity of the Internet and the increase in the number of netizens, it is of great significance to effectively monitor and predict the negative network public opinion on social media platforms. Most mainstream negative network public opinion early warning models mainly focus on mining textual features, while neglecting the response and dissemination among netizens during the process of negative network public opinion fermentation. This is one of the important reasons for the accuracy bottleneck of them. This article proposes a negative network public opinion early warning model based on evolutionary feature mining. Firstly, by using entity linking techniques based on knowledge graphs, domain prior knowledge is supplemented for the text need to be analyzed for negative network public opinion. Then, the text sentiment features extracted based on long short-term memory networks and the evolutionary features extracted based on graph convolutional networks are fused through an enhanced-feature fusion method. Finally, the features are used for classification and early warning. Experimental results based on the Weibo public dataset show that the EFM model has higher early warning performance for negative network public opinion compared to other baseline models. The accuracy of this model is 0.959. © 2023 IEEE.

关键词： Enhanced-feature fusion Evolutionary feature Knowledge graph Negative network public opinion Sentiment analysis

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A Method for Generating Medical Text Data by Integrating Spatiotemporal Attributes and Implicit State Transitions 9

A Method for Generating Medical Text Data by Integrating Spa...

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

作者： Yang, Xu Sun, Lijuan Gao, Yutong Guo, Sanchuan Wu, Xu Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Beijing University of Posts and Telecommunications School of Cyberspace Security BUPT Beijing China Beihang University Intellectual Property Information Service Center Beijing China Minzu University of China School of Information Engineering China

ISBN: (纸本)9798350391367

This study aims to generate medical text data that integrates spatiotemporal attributes to solve the problems of small size and low availability of open source text datasets due to the privacy of medical data. This paper optimizes the LeakGAN model by using a multi-layer LSTM structure and increasing the number of hidden and embedding layers to help generate networks that capture long-distance dependencies in data. In addition, we designed a spatiotemporal data generation method based on random algorithms and Markov chains to generate the spatiotemporal attributes of electronic medical records. In order to comprehensively evaluate the generation effect of the model, this paper evaluates from similarity-based indicators and likelihood-based indicators. The results show that the method designed in this article is 2.038 better than other models on NLL-oracle, 0.016 on NLL-test, and 0.0069 on EmbSim. The method proposed in this article has good ability to fit real data and can avoid the high cost and privacy leakage problems faced when manually constructing electronic medical record data sets by generating realistic medical electronic medical record synthetic data. This method also provides new solutions to the problem of insufficient data sets faced by more downstream applications (such as data auditing, anomaly detection, etc.). © 2024 IEEE.

关键词： Electronic health record

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Characterizing Developers' Behaviors in LLM -Supported Software Development

Characterizing Developers' Behaviors in LLM -Supported Softw...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Wei Wang Huilong Ning Shuo Qian Gaowei Zhang Yi Wang Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The emergence of large language models (LLMs) represented by ChatGPT has profoundly influenced the conventional software development process. Nevertheless, there is little research on how developers interact with LLMs. To investigate the interaction between developers and LLMs during software development, we conducted a user study with 56 participants, who were randomly assigned into two groups to perform different types of software development tasks. The first task was solving two simple coding puzzles, and the second was to fix two real-world bugs from a small-scale open source projects. We captured the full screen histories of all participants to construct a Markov activity transition model, depicting transitions among prevalent development activities such as coding, writing prompt and debugging. By characterizing developer behavior of the two groups of participants in the task, our study contributes empirical insights into developer behavior while interacting with LLMs. Such knowledge could guide the development of LLM -specific capabilities in supporting software development tasks, and offer valuable insights for developers aiming to utilize LLMs for more efficient problem-solving in their development practices.

关键词： Productivity Large language models Computer bugs Writing Chatbots Encoding Software

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An Effective Cloud Workflow Scheduling Approach Combining PSO and Idle Time Slot-Aware Rules

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IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1079-1094页

作者： Yun Wang Xingquan Zuo the School of Computing Science Beijing University of Posts and TelecommunicationsBeijing 100876China the School of Information Engineering Nanchang Institute of TechnologyNanchang 330099China the Key Laboratory of Trustworthy Distributed Computing and Service(BUPT) Ministry of EducationBeijing 100876China

Workflow scheduling is a key issue and remains a challenging problem in cloud *** with the large number of virtual machine(VM)types offered by cloud providers,cloud users need to choose the most appropriate VM type for each *** task scheduling sequences exist in a workflow *** task scheduling sequences have a significant impact on the scheduling *** is not easy to determine the most appropriate set of VM types for tasks and the best task scheduling ***,the idle time slots on VM instances should be used fully to increase resources'utilization and save the execution cost of a *** paper considers these three aspects simultaneously and proposes a cloud workflow scheduling approach which combines particle swarm optimization(PSO)and idle time slot-aware rules,to minimize the execution cost of a workflow application under a deadline constraint.A new particle encoding is devised to represent the VM type required by each task and the scheduling sequence of *** idle time slot-aware decoding procedure is proposed to decode a particle into a scheduling *** handle tasks'invalid priorities caused by the randomness of PSO,a repair method is used to repair those priorities to produce valid task scheduling *** proposed approach is compared with state-of-the-art cloud workflow scheduling *** show that the proposed approach outperforms the comparative algorithms in terms of both of the execution cost and the success rate in meeting the deadline.

关键词： Cloud computing idle time slot particle swarm optimization task scheduling sequence workflow scheduling

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Communication Encryption Scheme of 5G Power Trading Private Network Based on Dynamic key 2

Communication Encryption Scheme of 5G Power Trading Private ...

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2nd Asia Conference on Electrical, Power and Computer Engineering, EPCE 2023

作者： Zhou, Rui Song, Yiyang Xu, Ying Harbin Institute of Technology Electrical Engineering and Automation School Harbin China Guangdong Power Exchange Center Information Data Department Guangzhou China Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Beijing China

ISBN: (纸本)9798350310818

Security has been the primary concern for the 5G power trading private network, which involves a large amount of power trading information. The communication must be encrypted to prevent the leakage of important information. The communication encryption scheme for 5G power trading private network should meet the characteristics of small communication overhead, low storage space requirement and strong resistance to attack. Based on these characteristics, the dynamic key-based encryption scheme is designed. To ensure the security and reduce the computation overhead, the optimized symmetric encryption algorithm is used to update the shared key. And the dynamic key generation method is improved by adding random numbers to solve the problem that the correct transmission of the data link does not have true randomness. The encryption mode used can generate dynamic keys directly and synchronously at both ends of the communication, reducing the overhead of key exchange and eliminating the need for trusted third parties. The algorithm is also analyzed for security and efficiency and compared with other algorithms. The experimental results show that the improved algorithm enhances security with guaranteed efficiency. © 2023 IEEE.

关键词： Efficiency

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RL-MSA: a Reinforcement Learning-based Multi-line bus Scheduling Approach

arXiv

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arXiv 2024年

作者： Liu, Yingzhuo 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 China

Multiple Line Bus Scheduling Problem (MLBSP) is vital to save operational cost of bus company and guarantee service quality for passengers. Existing approaches typically generate a bus scheduling scheme in an offline manner and then schedule buses according to the scheme. In practice, uncertain events such as traffic congestion occur frequently, which may make the pre-determined bus scheduling scheme infeasible. In this paper, MLBSP is modeled as a Markov Decision Process (MDP). A Reinforcement Learning-based Multi-line bus Scheduling Approach (RL-MSA) is proposed for bus scheduling at both the offline and online phases. At the offline phase, deadhead decision is integrated into bus selection decision for the first time to simplify the learning problem. At the online phase, deadhead decision is made through a time window mechanism based on the policy learned at the offline phase. We develop several new and useful state features including the features for control points, bus lines and buses. A bus priority screening mechanism is invented to construct bus-related features. Considering the interests of both the bus company and passengers, a reward function combining the final reward and the step-wise reward is devised. Experiments at the offline phase demonstrate that the number of buses used of RL-MSA is decreased compared with offline optimization approaches. At the online phase, RL-MSA can cover all departure times in a timetable (i.e., service quality) without increasing the number of buses used (i.e., operational cost). Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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