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Intrusion Detection Based on Feature Reduction and Model Pruning in Electricity Trading Network

Journal of Computers (Taiwan) 2023年第5期34卷 213-227页

作者： Liu, Zhenzhen Zhou, Rui Huang, Kangqian Hu, Xin Jiang, Zhe Cai, Binsi Yuan, Kaiguo Information Data Department Guangdong Power Exchange Center Co. Ltd. Guangdong510080 China A Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing100876 China

The electricity trading network increases network flexibility and lowers trading costs with the aid of 5G and IOT technology. While it has improved trading efficiency and enhanced system intelligence, its security vulnerabilities pose significant challenges. In this study, we propose an intrusion detection method that focuses on feature reduction and model pruning in electricity trading network. The method effectively addresses the imbalance issue of the IDS2017 dataset by employing the SMOTE algorithm, reduces feature size and computational complexity through the application of PCA, autoencoder, and random forest techniques, and develops a lightweight intrusion detection model specifically designed for electricity trading network using model pruning and compression techniques. Experimental results demonstrate the effectiveness of the proposed model in detecting intrusions. The achieved precision, recall, F1 score, and false positive rate are at least 98.8%, 87.9%, 90.0%, and 0.08%, respectively. Furthermore, we conducted a comparative analysis of different pruning thresholds and determined that reducing the dimensionality to 49 dimensions yields superior model performance, making it particularly suitable for resource-constrained electricity trading network. © 2023 Computer Society of the Republic of China. All rights reserved.

关键词： Feature extraction

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Bag2image: A Multi-Instance Network Traffic Representation for Network Security Event Prediction

SSRN

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SSRN 2024年

作者： Zhang, Jiachen Han, Daoqi Lv, Zhaoxuan Liu, Yang Lu, Yueming Duan, Junke Zhang, Xinyu School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education China School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Astronautical Systems Engineering Beijing China

In practical scenarios, security events triggered by abnormal network traffic often result from the collective behavior of multiple data streams, embodying group security events with collective characteristics. Existing research methods, focusing on individual data streams, lack a macroscopic analysis and struggle with challenges of analyzing massive, imbalanced data sets. To address these challenges, this paper adopts a multi-instance learning approach, mapping multiple data streams into a bag with a coarse-grained approach, where each bag corresponds to a security event label and each data stream represents an instance. We propose a multi-instance network traffic conversion method, Bag2Image, which transforms temporal multi-instance network traffic data into image representations, preserving the spatio-temporal characteristics of instances within the bag through image channels and pixels. This strategy allows the network security event prediction task to be approached as an image classification problem, leveraging advanced image classification techniques for prediction. Our cross-experiments with six advanced multi-instance learning (MIL) algorithms and six different classification models demonstrate the superior performance of our method on both the UNSW-NB15 dataset and a private dataset. Specifically, our method achieved the highest F1 scores of 77.9% and 74.4% on these datasets, respectively, representing improvements of 4.1% and 13.5% over the second-best MIL algorithm. The recall rates also saw increases of 4.1% and 13.2%, respectively. © 2024, The Authors. All rights reserved.

关键词： Network security

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An Abnormal Behavior Detection Method Based on User Behavior Correlation Feature Sequence Modeling

An Abnormal Behavior Detection Method Based on User Behavior...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Yuting Zhou Lijuan Sun Jingchen Wu Yutong Gao Xu Wu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China Intellectual Property Information Service Center BHU Beihang University Beijing China School of Information Engineering MUC Minzu University of China Beijing China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

The healthcare industry is one of the industries most affected by insider threats. At present, the mainstream abnormal behavior detection of internal threats has the problems of relatively single reference dimension of user behavior data and lack of correlation analysis between data of different dimensions. Therefore, this paper proposes an abnormal behavior detection method that integrates user behavior correlation feature sequence modeling (UBCFSM). This method fully considers the multidimension and correlation of user behavior data, and uses the time convolutional network to learn behavior sequence features and the bidirectional long and short term memory network to learn behavior habit features of the convolutional attention mechanism. In addition, the deep neural network is introduced to fuse the deviations between the predicted results of different models and the actual results, detect abnormal results, and improve the generalization ability and detection accuracy of the model. Finally, experiments were carried out on the CERT public data set, and the AUC score of 97.1% was obtained. Compared with other abnormal behavior detection models, the proposed model has achieved significant improvement, showing its effectiveness and competitiveness.

关键词： Industries Deep learning Correlation Accuracy Fuses Medical services Predictive models Feature extraction Indexes Long short term memory

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Named Entity Recognition of Chinese Diabetic Literature by Integrating General Domain Knowledge 4

Named Entity Recognition of Chinese Diabetic Literature by I...

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4th IEEE International Conference on Computer and Communication Engineering Technology, CCET 2021

作者： Zhang, Duochuan Beijing University of Posts and Telecommunications School of Computer National Demonstrative Software School Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Ministry of Education Beijing China

ISBN: (纸本)9781665438902

In the field of named entity recognition of diabetes literature, due to the lack of large-scale and high-quality annotation data sets, the traditional named entity recognition technology has the drawback of insufficient language feature extraction and poor generalization ability. In view of the above problems, this paper proposes an entity recognition method that integrates general domain knowledge for naming entity recognition in diabetes literature. This method uses Word2vec algorithm to generate feature level vectors based on Chinese diabetes literature. On this basis, the feature vectors are fused with the feature vectors generated by the pretraining model of BERT on the Chinese Wikipedia corpus, and uses principal component analysis to reduce the redundant feature vectors and enhance the expression ability of feature vectors in the general language scope. Then the context features are extracted by bidirectional long-term and short-term memory network, and finally the entity label sequence is generated by conditional random field decoding. The experiment proves that the model which integrates general domain knowledge is better than other traditional models in the recognition of Chinese diabetes literature, and the highest F1 value is 71.08%. © 2021 IEEE.

关键词： Domain Knowledge

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Integrated Beamforming and Resource Allocation in RIS-Assisted mmWave Networks based on Deep Reinforcement Learning

Integrated Beamforming and Resource Allocation in RIS-Assist...

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Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Di Chen Hui Gao Na Chen Ruohan Cao Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Japan

Reconfigurable intelligent surface (RIS) has gained wide attention recently as a promising solution to address the blockage issue in millimeter wave (mmWave) communication networks. In this paper, we consider a RIS-assisted mmWave communication system consisting of multiple RISs and users. A novel integrated scheme based on deep reinforcement learning (DRL) is proposed to jointly optimize the hybrid beamforming, phase shifts, and power allocation. Specifically, we design a multi-variable action output to achieve integrated optimization in both beam and power domains. We also design a weighted sum reward to balance the influence of beam and power domain optimizing variables in the training process. Experimental results demonstrate that the proposed DRL-based method can achieve at least 37% higher sum rates than the baseline algorithm.

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Toward Wisdom-Evolutionary and Primitive-Concise 6G:A New Paradigm of Semantic Communication Networks

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Engineering 2022年第1期8卷 60-73页

作者： Ping Zhang Wenjun Xu Hui Gao Kai Niu Xiaodong Xu Xiaoqi Qin Caixia Yuan Zhijin Qin Haitao Zhao Jibo Wei Fangwei Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Peng Cheng Laboratory Shenzhen 518066China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China The School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China School of Humanities Beijing University of Posts and TelecommunicationsBeijing 100876China

The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological *** this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G *** particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network *** the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information *** proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in *** features a lower bandwidth requirement,less redundancy,and more accurate intent *** also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.

关键词： 6G Semantic information Semantic communication Intelligent communication

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

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

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IEEE International Conference on Data Science in Cyberspace (DSC)

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

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

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.).

关键词： Data privacy Cyberspace Data collection Data science Data models Spatiotemporal phenomena Electronic medical records Usability Long short term memory Synthetic data

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Robust text clustering with graph and textual adversarial learning 5

Robust text clustering with graph and textual adversarial le...

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5th IEEE International Conference on Data Science in Cyberspace, DSC 2020

作者： Liang, Yongming Tian, Tian Jin, Kaifu Yang, Xiaoyu Lv, Yuefei Zhang, Xi Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China

ISBN: (纸本)9781728195582

Text clustering is a fundamental task that finds groups of similar texts in the corpus. Deep learning based models can capture the semantic and syntactic information in local word sequences to represent the texts but may ignore global text structures in the corpus. Recently, Graph Convolutional Networks (GCNs) have achieved the state-of-the-art performance in many applications, which may facilitate the text clustering task by exploiting the graph structures between texts via words and phrases. However, GCNs are vulnerable to adversarial attacks, e.g., small perturbations in graph structures and node attributes lead to poor performance. To address this issue, we propose a Robust Text Clustering (RTC) approach which consists of both autoencoder and GCN modules to exploit the local text features and global text structures respectively in one unified framework. We generate two types of adversarial perturbations targeting text structure and graph structure, and develop an adversarial training method to improve the robustness and generalization of the RTC framework. Extensive experiments on two real-world datasets demonstrate the effectiveness of our proposal in text clustering. © 2020 IEEE.

关键词： Semantics

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Mask Neural Network for Predicting Flight Ticket Price 6th

Mask Neural Network for Predicting Flight Ticket Price

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6th Conference on Signal and Information Processing, Networking and Computers, ICSINC 2019

作者： Zhang, Qianqian He, Yuan Jing, Xiaojun Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789811541629

Recently, the global air traffic has increased rapidly, most passengers choose to buy tickets in their own experiences. So accurately predicting flight ticket price is of great significance. Recently there exist some works on flight ticket price prediction. However, these works pay attention to solve a single flight price prediction. However, it’s unrealistic to build a lot of models for different flights. In this paper, we consider a lot of factors, including additional service data, flight data and time data. Firstly, we analyze data and extract flight information and time features. Furthermore, we propose a model: mask neural network, to solve flight ticket price prediction, which divides the features into two parts: individual information and general information. General information represents the portrait of a certain flight and individual information represents features of a certain flight. Since every flight has its own distribution, we cannot put individual information into the network directly. So we introduce mask concept and use general information as a mask to filter individual information. Experiments on a competition dataset with 3 months datasets introduce the effectiveness of our approaches, that can achieve the state-of-the-art in ticket price prediction. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Not All Image Regions Matter: Masked Vector Quantization for...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Mengqi Huang Zhendong Mao Quan Wang Yongdong Zhang University of Science and Technology of China Hefei China Institute of Artificial intelligence Hefei Comprehensive National Science Center Hefei China MOE Key Laboratory of Trustworthy Beijing University of Posts and Telecommunications Distributed Computing and Service Beijing China

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQVAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://***/CrossmodalGroup/MaskedVectorQuantization.

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