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Asia-Pacific network Operations and Management Symposium (APNOMS)

作者： Ye Zhang Wei Zhang Hao Hao Kai Zhang Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

To meet the growing traffic demand and relieve the pressure on the core network, edge caching and device-to-device (D2D) communication are used in next generation mobile networks. In this paper, based on a three-way collaboration between user devices (UEs), base stations (BSs) and cloud data center, caching the content most likely to be requested can facilitate direct content delivery without the need to fetch content from remote cloud data center in the mobile edge networks. This reduces the average retrieval latency of content, reduces the backhaul burden and minimises duplicate content delivery. Specifically, we first use a spectral clustering algorithm to cluster users based on their social and physical attributes, and then associate each cluster with a different base station in order to efficiently cache popular contents. Finally, we propose a new caching policy based on content cache value and user activity to take full account of the metrics that influence caching decisions. Experimental results show that our proposed policy can effectively reduce content access latency.

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Job2Vec: A Self-Supervised Contrastive Learning Based HPC Job Power Consumption Prediction Framework

Job2Vec: A Self-Supervised Contrastive Learning Based HPC Jo...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jie Zhang Jian Song Xiang Li Xuesen Tian Zhigang Zhao Lu Wu Chunxiao Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In the context of the rapid development of big data and artificial intelligence, the field of HPC is facing significant challenges in energy consumption. To address this challenge, this paper proposes a power consumption prediction framework for HPC jobs based on self-supervised contrastive learning, named Job2Vec. Firstly, HPC jobs are clustered into different clusters based on job logs and power consumption curve shapes. Next, high-quality features of power consumption data at different granularities within each cluster are captured through self-supervised contrastive learning. Lastly, an incremental update strategy is introduced to handle the dynamic changes in HPC data. The experimental results demonstrate that, on the Jinan Supercomputing dataset comprising 1170 HPC jobs, the Job2Vec framework outperforms the baseline models overall in terms of prediction performance. Additionally, we also investigated the model’s performance in single-step and multi-step forecasting, the impact of different indicators on prediction accuracy, and conducted ablation experiments to confirm the indispensability of each module. This is the first time that self-supervised contrastive learning has been introduced into the HPC field, aiming to provide support for energy scheduling in data centers.

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Few-Shot Hyperspectral Image Classification Based on Cross-Domain Spectral Semantic Relation Transformer

Few-Shot Hyperspectral Image Classification Based on Cross-D...

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IEEE International Conference on Image Processing

作者： Mengxin Cao Guixin Zhao Aimei Dong Guohua Lv Ying Guo Xiangjun Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

In practical hyperspectral image (HSI) classification tasks, we often encounter the problems of few-shot classification and domain misalignment between source domains and target domains. To solve this classification paradigm, a meta-learning method of few-shot learning (FSL) is usually used. However, most existing FSL methods address the problem for domain alignment and neglect the exploration of semantic relationships of objects across domains. In this paper, we propose the cross-domain spectral semantic transformer FSL (SSTFSL), which can fully extract semantic features and spectral detail features for the cross-domain few-shot HSI classification task. Specifically, the multi-head self-attention (MSA) mechanism with enhancement process (EP) of the transformer is used to map out semantically relevant local regions and can enhance the ability of the model to distinguish subtle feature differences in the spectrum. In addition, the matching degree of different branches is computed by relational network learning, which ultimately enables cross-domain few-shot HSI classification. Through extensive experiments, we evaluate the classification performance of SSTFSL on HSI datasets. The results demonstrate that SSTFSL outperforms existing FSL methods and deep learning methods on HSI classification.

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Cross Modal Sentiment Classification of Social Media Based on Meta Heuristic Algorithm

Cross Modal Sentiment Classification of Social Media Based o...

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International Conference on Computer Supported Cooperative Work in Design

作者： Wantong Du Wanli Min Yushui Geng Hu Liang Hehu Zhou Ministry of Education Shandong Computer Science Center Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Synthesis Electronic Technology Co. Ltd

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The evolution of online social media has reshaped public behaviors, and social data infused with emotions provides crucial decision support for sentiment analysis tasks. Conventional multimodal approaches, influenced by redundant information in feature extraction during sentiment analysis, often focus solely on modality interactions without considering the unique content of each modality. This paper introduces the HGB-HFN model, addressing feature redundancy by optimizing extracted features using metaheuristic algorithm techniques, achieving a higher accuracy optimal feature dataset at lower computational costs. To counteract the neglect of modal-independent information, a hybrid fusion approach is adopted. It prioritizes visual features to extract precise semantic and emotional information from textual content, training multiple base classifiers to learn independent and diverse discriminative information. Comparative experiments on streaming datasets MOSI and MOSEI demonstrate the computational approach’s superiority in both accuracy and efficiency over existing methods.

关键词： Training Sentiment analysis Visualization Accuracy Social networking (online) Metaheuristics Semantics

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Detecting Inconsistent Vulnerable Software Version in security Vulnerability Reports 1

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4th International Conference on Frontiers in Cyber security, FCS 2021

作者： Ren, Hansong Li, Xuejun Lei, Liao Ou, Guoliang Sun, Hongyu Wu, Gaofei Tian, Xiao Hu, Jinglu Zhang, Yuqing School of Cyber Engineering Xidian University Shaanxi Xian710126 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guangxi Guilin541010 China National Computer Network Intrusion Prevention Center University of Chinese Academy of Sciences Beijing100049 China School of Information Production and Systems Waseda University Tokyo169-805 Japan College of Computer and Cyberspace Security Hainan University Haikou570228 China

ISBN: (数字)9789811905230

ISBN: (纸本)9789811905223

At present, the vulnerability database research has mainly focused on whether the disclosed information is accurate. However, the information differences between the various vulnerability databases have received little attention. This article proposes a WITTY (softWare versIon inconsisTency measuremenT sYstem) to detect the differences between the affected software versions of NVD and different language vulnerability databases (including English CVE, OpenWall, Chinese CNNVD, CNVD, and other eight databases). WITTY can enable Our large-scale quantitative information consistency. We introduce named entity recognition (NER) and relation extraction (RE) based on deep learning. We present custom design into named entity recognition (NER) and relation extraction (RE) based on deep learning, enabling WITTY to recognize previously invisible software names and versions based on sentence structure and context. Ground-truth shows that the system has a high accuracy rate (95.3% accuracy rate, 89.9% recall rate). We use data from 8 vulnerability databases in the past 21 years, involving 554,725 vulnerability reports. The results show that they are inconsistent. The software version is prevalent. The average exact match rate of English vulnerability databases CVE, OpenWall, and other vulnerability databases with cve is only 22.1%. The average exact match rate of Chinese CNNVD and CNVD is 49.5%, and the excat match rate of Russian vulnerability databases is 25.8%. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Database systems

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A Lithium-Ion Battery Health State Assessment and Remaining Useful Life Prediction Method Based on Multi Slope Features

SSRN

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SSRN 2024年

作者： Chen, Gaige Zhong, Siqi Yang, Yahong Zhang, Xiaoshen Deng, Wei Liu, Jinsong School of Communications and Information Engineering School of Artificial Intelligence Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Union Research Center of University and Enterprise for 5G+ Industrial Internet Communication Terminal Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Shaanxi Xi’an710121 China State Key Laboratory of Space Power Sources Shanghai Institute of Space Power-sources Shanghai200245 China Xi'an Aviation Brake Technology Co. Ltd Shaanxi Xi’an710065 China Contemporary Amperex Technology Co. Ltd. Fujian Ningde352107 China

In the field of battery management, accurately predicting the remaining useful life (RUL) of lithium-ion batteries is critical to guarding battery and device safety. However, current prediction methods are mainly based on battery voltage, current, temperature and other data, and there is a challenge problem with neglecting the intrinsic connection between these data. To address this problem, a lithium-ion battery health state assessment and remaining useful life prediction method based on multi slope features of discharge curves is proposed. First, new slope features are proposed based on the lithium-ion battery discharge mechanism;second, slope features are utilized to construct health indicator (HI) and estimate state of health (SOH);then, unsupervised clustering of unlabeled state of health data is performed to construct a health state assessment model;and finally, the remaining useful life prediction model is constructed based on grey wolf optimizer and gated recurrent unit (GWO-GRU). The proposed method is validated using the aging data of lithium-ion batteries released by NASA Ames Research Center, and the dominant is emphasized by comparison with other important methods. The results show that the method can effectively assess the health state of lithium-ion batteries and accurately predict their remaining useful life, which can provide support for the realization of lithium-ion battery health management and intelligent operation and maintenance. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Detection method of trusted blockchain link flood attack based on trusted execution environment

Detection method of trusted blockchain link flood attack bas...

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Information technology: IoT and Smart City (ITIoTSC), International Conference on

作者： Ruixue Kuang Lianhai Wang Shuhui Zhang Shujiang Xu Wei Shao Qizheng Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (数字)9798350328349

ISBN: (纸本)9798350328356

In order to improve the detection accuracy and improve the detection speed, this paper designs a detection method of trusted blockchain link flood attack based on the trusted execution environment. First, above all, analyze the trusted block chain structure, then design the trusted block chain link flood attack detection algorithm, and finally the trusted execution environment is used to realize the trusted block chain link flood attack detection. The experimental results prove that the false alarm rate and leakage detection rate of the detection method of the flood flood attack designed based on the trusted execution environment are both low, with the lowest value only 3% and 0.98%. The detection accuracy is high, the detection time is short, and the detection can be completed in at least 13s. It can be proved that the performance of this design method is better and has certain application value.

关键词： Accuracy Information resources Smart cities Computer hacking Design methodology Blockchains Floods

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Joint Secure and Covert Communications for Active STAR-RIS Assisted ISAC Systems

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IEEE Transactions on Wireless Communications 2025年

作者： Guo, Liang Jia, Jie Mu, Xidong Liu, Yuanwei Chen, Jian Wang, Xingwei Northeastern University School of Computer Science and Engineering China Northeastern University Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China BelfastBT3 9DT United Kingdom Department of Electrical and Electronic Engineering China

This paper investigates the design of jointly supporting physical layer security (PLS) and covert communications (CCs) in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (a-STAR-RIS) assisted integrated sensing and communication (ISAC) system. Due to the unified waveform design of ISAC signals, we consider a challenging scenario with two targets being suspicious attackers, where one warden target potentially detects the confidential transmission behavior of covert users and another eavesdropper target attempts to intercept the broadcasted confidential information of security users. We investigate the joint beamforming design at the base station (BS) and the a-STAR-RIS to achieve a high-quality sensing beampattern while meeting covertness and security communication requirements. (1) For the ideal scenario with perfect channel state information (CSI) and precise target locations, we propose an alternative optimization (AO) method to address the optimization problem involving highly coupled variables. Specifically, the optimal beamforming design at the BS is handled using the semi-definite relaxation (SDR) technique, while the beamforming design at the a-STAR-RIS is addressed through a penalty-based iterative algorithm. (2) A more practical case with uncertain target locations and imperfect CSI is considered to achieve a robust beamforming design, where the non-deterministic outage probability constraints are effectively transformed by employing the Bernstein-type inequality. Numerical results demonstrate the superiority of the a-STAR-RIS over the baseline cases and certify that the proposed algorithms can effectively balance the tradeoff among the sensing quality, covert and secure communication requirements. Besides, results also show that the proposed robust beamforming scheme can construct adequate sensing beampattern, even with imperfect CSI and uncertain target locations. © 2002-2012 IEEE.

关键词： Beamforming

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A Web Attack Detection Method Based on DistilBERT and Feature Fusion for Power Micro-Application Server

A Web Attack Detection Method Based on DistilBERT and Featur...

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Advanced Electronics, Electrical and Green Energy (AEEGE), International Conference on

作者： Li Nige Chen Lu Zhao Lei Teng Zhenning Wang Zhiqiang Shao Yiyang Gui Xiaolin State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Power cyber-Security Protection and Monitoring Technology Nanjing China Shaanxi Province Key Laboratory of Computer Network Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China

The widespread use of power micro-applications has significantly expanded the attack surface of their server side, thus increasing the risk of vulnerability attacks such as SQL injection, XSS, and CRLF injection. To ensure the security of power systems, these attacks must be detected precisely and timely. Therefore, this paper proposes a server-side web attack detection method based on DistilBERT and feature fusion for HTTP and HTTPS requests. The method treats HTTP and HTTPS requests as text data, specifically, extracts deep semantic features using DistilBERT, and fuses them with well-designed empirical features to comprehensively characterize and classify the request. Consequently, anomalous requests can be detected. The experimental results show that the accuracy, precision, recall, and F1 score of the method on HTTP CSIC 2010 and FWAF datasets are close to or higher than 99%. Compared with other methods as far as we known, this method has better performance and efficiency.

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A Study on a Reduced Indicator System for the network security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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International Conference on networking and network Applications (NaNA)

作者： Hongbin Zhang Yikang Bai Dongmei Zhao Bin Liu Ying Xu Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time.

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