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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Luqin Lv Yueming Lu School of Cyberspace Security Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450387828

Aiming at the problem that single protection technology is difficult to meet the needs of network security, the rationality and accuracy of situation assessment index are poor, the multi-source and multi-dimensional network security situation awareness model and multi-source data association analysis method are proposed. This model combines with data acquisition, data preprocessing, abnormal analysis, security threat assessment and situation visualization. By using Apriori algorithm to mine the relationship between multiple sources of data, the strong rules are filtered out. The experimental results show that the model can accurately perceive known intrusion, unknown threat and its own vulnerability.

关键词： Apriori algorithm

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Web Attack Detection through Network-Traffic-Based Feature Engineering and Machine Learning 2020

Web Attack Detection through Network-Traffic-Based Feature E...

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Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Jian Yang Hao Wang Yueming Lu School of Cyberspace Security Key Lab of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781450387828

The design of feature set is a basic research direction of anomaly detection technology. The characterization ability of feature set directly affects the accuracy and generalization ability of intrusion detection algorithm. KDD CUP 99 is the most widely used data set in anomaly detection related research papers, but the feature set needs to analyze the payload content characteristics of packets, so the feature set cannot be applied to the encrypted traffic. CICIDS2017 is also a widely used data set. However, because data in PCAP format can only be used as input, the generalization ability of the model trained by this data set is poor when it is used for real-time attack *** on the above problems, a feature set based on network flow characteristics is designed and implemented in this paper, which includes the time-window-based statistical features related to network traffic and host-related statistical features. Experiments show that the anomaly detection model trained by the feature set can meet the requirements of both real-time and classification accuracy.

关键词： Machine learning KDD CUP 99 CICIDS2017 Network traffic feature set Intrusion detection

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TNRAS: Tor Nodes Reliability Analysis Scheme 2021

TNRAS: Tor Nodes Reliability Analysis Scheme

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2021 the 11th International Conference on Communication and Network Security

作者： Wenzhen Zhang Tianbo Lu Zeyu Du School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications China and Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing University of Posts and Telecommunications China School of Computer Science (National Pilot Software Engineering School) School of Cyberspace Security Beijing University of Posts and Telecommunications China and Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing University of Posts and Telecommunications China

ISBN: (纸本)9781450386425

With the rapid Internet development, the security attacks become more and more noteworthy that people have paid more concentration on personal privacy. The Onion Router (Tor), a distributed anonymous communication network, has gradually come into our field of vision for secure communicating on the Internet. However, there are still several security issues like the Sybil attack in Tor, which pose a non-negligible threat to both the network and its users. This paper proposes an ideal method Tor nodes reliability analysis scheme (TNRAS), Tor nodes reliability analysis scheme, against the Sybil attack. We explore an effective mean to reveal the Sybil or unreliable nodes in Tor. Through analyzing the general characteristics of nodes from six aspects, we can predict the unreliable nodes. We filter them and assess the remaining high reliable nodes for practical application afterwards. After that, the Tor reliable nodes analysis scheme application is implemented and analyzed. Moreover, this paper points out the definition of the Sybil nodes, high reliable nodes, and the reliability, then further assessing the nodes’ reliability. The research reveals the Sybil nodes characteristics through our six aspects detection method, and the predict model achieves to 0.928 training correlation and 0.715 validation correlation both with more than 0.8 ROC, which is of great significance for Tor with its increasing number of users and the worsening network context.

关键词： reliable nodes anonymous communication the Sybil attack Tor reliability

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Auto-GAN: GAN-Based Self-Supervised Collaborative Learning for Robust Spatio-Temporal Trajectory Classification in IoT

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IEEE Internet of Things Journal 2025年

作者： Jia, Jia Li, Linghui Li, Ximing Wang, Ning Cai, Binsi Zhang, Xu Qiu, Pengfei Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Research Institute of China Mobile Communications Corporation Beijing100032 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China China Academy of Information and Communications Technology Beijing China Nanjing University School of Electronic Science and Engineering Nanjing210023 China

With the rapid proliferation of crowd mobility data produced by ubiquitous mobile devices equipped with spatial positioning modules, deep neural networks (DNNs) have become widely applied in spatio-temporal trajectory modeling. However, recent studies have shown that DNNs are vulnerable to adversarial examples with strong transferability, which are crafted by introducing small perturbations to original examples but can cause catastrophic mistakes. To mitigate this vulnerability and enhance model robustness, we propose a novel self-supervised collaborative learning framework named Auto-GAN that consists of a generator for automatically learning robust latent features and a discriminator for providing comprehensive guidance to the generator. By leveraging the collaboration between the generator and discriminator, our proposed method significantly improves the denoising performance. Moreover, we combine point-level and feature-level constraints into training processes between original example reconstruction and adversarial example denoising, thereby effectively suppressing the potential "error amplification effect". Extensive experiments conducted on two representative real-world mobility datasets show that our proposed method can significantly enhance the model’s robustness against various adversarial attacks, while preserving the model’s prediction accuracy on original examples. © 2014 IEEE.

关键词： Self-supervised learning

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MASCA: A Multimodal Abstractive Summarization Model Based on Core Words Fusion Attention 7

MASCA: A Multimodal Abstractive Summarization Model Based on...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Yin, Xiaohan Sun, Lijuan Wu, Jingchen Gao, Yutong Wu, Xu Qiu, Lirong 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 Bupt Beijing China Beijing Jiaotong University Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

With the rapid development of information technology, automatic summarization research has gradually developed into abstractive summarization. The number of data in various modalities(such as pictures) in practical applications is increasing, but existing summarization methods still focus mainly on textual data and ignore other modal data. At the same time, no attention was paid to the key information contained in original data in past studies. In order to solve the above problems and make full use of multimodal data, this paper proposes a multimodal abstractive summarization model based on core words fusion attention, named MASCA. MASCA adopts a special dual encoder-decoder structure. It adds BART and ResNetl01 to the traditional encoder-decoder model. Firstly, each modality is encoded separately to obtain the corresponding modal representation, and secondly, it is decoded to obtain the intermediate summary in text form. Finally, the core words and semantic information are fused in a second encoder-decoder module that incorporates the attention mechanism to generate the final abstract. The experimental results on the Yelp dataset demonstrate the effectiveness and superiority of the method designed in this paper. © 2022 IEEE.

关键词： Semantics

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Meta Balanced Network for Fair Face Recognition

arXiv

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arXiv 2022年

作者： Wang, Mei Zhang, Yaobin Deng, Weihong The Pattern Recognition and Intelligent System Lab. School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China Key Lab. of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this paper, we aim to systematically and scientifically study this bias from both data and algorithm aspects. First, using the dermatologist approved Fitzpatrick Skin Type classification system and Individual Typology Angle, we contribute a benchmark called Identity Shades (IDS) database, which effectively quantifies the degree of the bias with respect to skin tone in existing face recognition algorithms and commercial APIs. Further, we provide two skin-tone aware training datasets, called BUPT-Globalface dataset and BUPT-Balancedface dataset, to remove bias in training data. Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones. To determine the margins, our method optimizes a meta skewness loss on a clean and unbiased meta set and utilizes backward-on-backward automatic differentiation to perform a second order gradient descent step on the current margins. Extensive experiments show that MBN successfully mitigates bias and learns more balanced performance for people with different skin tones in face recognition. The proposed datasets are available at http://***/RFW/***. © 2022, CC BY.

关键词： Face recognition

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Vehicle Destination Prediction Based on Trajectory Data

Vehicle Destination Prediction Based on Trajectory Data

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Neural Networks, Information and Communication Engineering (NNICE), International Conference on

作者： Wenming Guo Ruiqi Jia Che Zong Ruichao Li Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China School of Information Engineering Xinjiang Institute of Engineering Urumqi China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

With the increasing requirements of real-time traffic management in urban transportation, vehicle destination prediction data, as an important part of smart city brain data, is becoming more and more important in urban traffic management and operation. This paper builds a vehicle destination prediction scheme based on trajectory data. By collecting public vehicle trajectory datasets, the Sequence to Sequence (Seq2Seq) sequence generation model and the Text-Convolutional Neural Network (Text-CNN) sequence classification model are built respectively to implement the vehicle destination prediction. In order to evaluate the performance of the models, Accuracy and Mean Distance are used as metrics to compare the model performance under different training data conditions. Advantage analysis was done in combination with different application scenarios for practical application reference. According to the prediction performance of different models built in this work in different application scenarios, the advantages are analyzed for reference in practice.

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The Mechanism of Mobile Communication Technology Prompting Digital Transformation of Manufacturing Industry: From the Perspective of Data Factor Empowerment 24

The Mechanism of Mobile Communication Technology Prompting D...

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Proceedings of the 2024 10th International Conference on Communication and Information Processing

作者： Ran Li Muhua Wei Huili Liu Jiayin Qi Lina Liu Department of User and Market Research China Mobile Research Institute Beijing Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou Guangzhou China and Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing Beijing China School of Economics and Management Beijing University of Posts and Telecommunications Beijing Beijing China

ISBN: (纸本)9798400717444

Abstract: The digital transformation of manufacturing industry is a crucial measure for achieving high-quality development, and mobile communication technology plays a significant role in this process. This paper focuses on exploring the internal mechanism of mobile communication technology promoting the digital transformation of manufacturing industry, with a particular emphasis on three major technologies: 5G, 5G-A, and 6G. Firstly, the paper summarizes the latest progress of mobile communication technology in the field of manufacturing digital transformation. Secondly, it outlines the characteristics of technological evolution from 5G to 6G. Thirdly, based on the technological evolution process, it presents the three major capabilities of 6G, namely transmission, integrated sensing and communication, and intelligent computing. It further elaborates on how mobile communication technology gives rise to data facotor and clarifies the internal mechanism of mobile communication technology enabling data factor to empower the digital transformation of manufacturing industry. Finally, it looks forward to the typical development trends of 6G in the field of manufacturing digital transformation.

关键词： 5G

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Audio Public opinion Analysis Model based on heterogeneous Neural Network

Audio Public opinion Analysis Model based on heterogeneous N...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Haikun Jiang Xu Wu Xiaqing Xie Jingchen Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Library Beijing China

ISBN: (数字)9781728183190

ISBN: (纸本)9781728183206

With the rapid development of the information age, the main body of public opinion information is no longer just structured data such as text and numbers, but the proportion of unstructured data such as audio and video has increased significantly. Therefore, the analysis and processing of unstructured public opinion data has become the key to deal with the problem of public opinion. Based on this, this paper designs an audio public opinion early warning model based on heterogeneous neural network. Heterogeneous neural network refers to a variety of different types of neural networks to participate in the completion of specified tasks under the premise of data sharing, while retaining the autonomy of each neural network. In this paper, the audio data is converted into a spectrum as an input feature. The convolution neural network can obtain the feature representation of the audio in the spectrum graph, which is used as the input of the recurrent neural network, and then the features related to the text representation are obtained. In this paper, Bert is used to modify the semantic results combined with public opinion domain knowledge, and finally the results are introduced into the shallow neural network Fasttext for public opinion early warning processing. In this paper, experiments are carried out based on the open source voice data set ST-CMDS-20170001-1 Chinese mandarin corpus and Pytorch platform. The results show that this model can effectively analyze and process public opinion of unstructured audio and video data.

关键词： Knowledge engineering Analytical models Recurrent neural networks Computational modeling Neural networks Semantics Task analysis

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

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

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Ning Su Lijuan Sun Yutong Gao Jingchen Wu Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT School of Economics and Management Beijing University of Posts and Telecommunications Beijing China Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT

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.

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