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2nd International Conference on Advanced Electronics, Electrical and Green Energy, AEEGE 2023

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

ISBN: (纸本)9798350322866

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. © 2023 IEEE.

关键词： Cybersecurity

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News-driven financial warning based on label information attention 5

News-driven financial warning based on label information att...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Zhilin, Ren Long, Chen Xiaohua, Huang Wenjing, Wang Cai, Xu Wei, Zhao Ziyu, Guan Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Most existing news-driven stock market prediction methods ignore the potential relationship between financial news and stocks. The complex relationship can help us to improve the accuracy of algorithmic trading systems. Therefore, we propose a deep learning method for financial warning by fusing Stock Label Information (SLI). We extract events from news texts and fuse stock information together as feature vectors, using neural networks to model the underlying relationship between news and stocks. Experimental results show that our method outperforms other baseline methods in experiments on TPX500 and TPX100 datasets. CCS CONCEPTS •computing methodologies •Artificial intelligence •Natural language processing © 2022 ACM.

关键词： Deep learning

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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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2023 International Conference on networking and network Applications, NaNA 2023

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

ISBN: (纸本)9798350327380

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. © 2023 IEEE.

关键词： network security

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Source-free Domain Adaptation with Multiple Alignment for Efficient Image Retrieval

Source-free Domain Adaptation with Multiple Alignment for Ef...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jinghui Ni Hui Cui Lihai Zhao Fengling Li Xiaohui Han Lijuan Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Quan Cheng Laboratory Shandong Open University University of Technology Sydney

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Domain adaptation techniques help models generalize to target domains by addressing domain discrepancies between the source and target domain data distributions. These techniques are particularly valuable for cross-domain hashing retrieval, as they reduce training costs while maintaining high retrieval efficiency. However, existing unsupervised domain adaptative hashing methods often require access to both source and target domain data, which may raise privacy concerns regarding source domain data. To address these concerns, restricting access to source domain data is crucial, but this restriction also makes the alignment process between domains more challenging. In this paper, we propose a Source-free Domain Adaptive Hashing with Multiple Alignment (SFDAH-MA) approach for image retrieval. SFDAH-MA integrates model structure alignment, class moment alignment, and semantic relationship alignment to maximize inter-domain knowledge transfer. This comprehensive alignment strategy not only enhances retrieval performance across domains but also minimizes reliance on source domain data, thereby supporting data privacy protection. Extensive experiments show that SFDAH-MA achieves state-of-the-art performance in source-free settings and achieves comparable results to existing unsupervised domain adaptive hashing methods under conventional settings. The source codes of our method are available at: https://***/Nikphyc/SFDAH-MA.

关键词： Training Data privacy Adaptation models Source coding Semantics Image retrieval Signal processing Protection Speech processing Knowledge transfer

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Exploration and practice on the construction of computer first-class major 16

Exploration and practice on the construction of computer fir...

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16th IEEE International Conference on Computer Science and Education, ICCSE 2021

作者： Li, Wei Hei, Xinhong Wang, Lei Wang, Xiaofan Xi'An University of Technology School of Computer Science and Engineering Xi'an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Shaanxi University of Technology School of Mathematics and Computer Science Hanzhong China

ISBN: (纸本)9781665414685

This paper analyzes the current situation of the construction of the first-class computer major, and puts forward the construction scheme of the first-class computer major in local colleges based on the concept of outcome based education (OBE). The reform scheme of the construction of the first-class computer major is elaborated from four aspects: training objectives, curriculum system, teaching mode, and evaluation mechanism. © 2021 IEEE.

关键词： Training Computer science Knowledge engineering Problem-solving System analysis and design

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RDGFuzz: A directed greybox fuzzing optimization method based on Rich-Branch nodes 6

RDGFuzz: A directed greybox fuzzing optimization method base...

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6th International Conference on Electronic Information technology and Computer Engineering, EITCE 2022

作者： Wu, Zejun Lu, Li Jia, Qiong Chen, Zhihao State Key Laboratory of Mathematical Engineering and advanced Computing Zheng zhou China National Local Joint Engineering Laboratory of Network Space Security Technology Zheng zhou China Beijing Institute of Computer Technology and Application BeiJing China

ISBN: (纸本)9781450397148

Directed fuzzing technology is one of the key technologies to quickly reach a specific location of software, and to conduct targeted testing or bug recurrence. However, directed fuzzing technology has some problems, such as unreasonable seed energy allocation, low code coverage and incomplete testing. To solve the above problems, this paper proposes an optimization method of directed fuzzing based on Rich-Branch nodes. In this method, the concept of Rich-Branch nodes is defined and the algorithm of extracting Rich-Branch nodes is given. The optimization method collects the coverage information of the target program in the running process, calculates the weights of covered functions and nodes in real time by combining CG and CFG of the target program, and generates a list of Rich-Branch nodes. According to the weights of Rich-Branch nodes, the seed energy allocation algorithm of AFLGo is optimized and improved. Compared with AFLGo, this optimization method improves the average code coverage of each targeted point by 56.79%, and has the same target reaching ability as AFLGo. © 2022 Association for computing Machinery.

关键词： Software testing

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Automatic skin lesion classification using a novel densely connected convolutional network integrated with an attention module

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 8943-8956页

作者： Shan, Pufang Chen, Jialei Fu, Chong Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science The University of Auckland Auckland New Zealand

Automated skin lesion classification in dermoscopy images remains challenging due to the existence of artefacts and intrinsic cutaneous features, diversity of lesion morphology, insufficiency of training data, and class imbalance problem. To address these challenges, we propose a new densely connected convolutional network termed AttDenseNet-121, which is obtained by integrating the convolutional block attention module (CBAM) into DenseNet-121. CBAM is a simple yet efficient attention module, which is further improved by adding a novel pooling layer (multiscale-pooling) to effectively enhance its attention capability. The optimized CBAM strengthens the representation power of DenseNet-121 by emphasizing meaningful features and suppressing unnecessary ones, thus significantly enhancing the classification performance. Besides, to handle the imbalanced dataset, we employ an improved focal loss function rather than the traditional cross-entropy loss function to train AttDenseNet-121. The improved focal loss is calculated from a different perspective compared with the original focal loss. It makes the distribution of positive samples and negative samples in each batch the same as that in the original data, successfully mitigating the negative influence of class imbalance on this multi-class classification task. We conduct extensive experiments on the public benchmark dataset (HAM10000) and the results indicate that our proposed method achieves superior performance compared with that of the baselines and state-of-the-art algorithms. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Convolution

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Prompt and Contrastive Learning for Few-shot Sentiment Classification 5

Prompt and Contrastive Learning for Few-shot Sentiment Class...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Wang, Fei Chen, Long Huang, Xiaohua Xu, Cai Zhao, Wei Guan, Ziyu Lu, Guangyue Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications China School of Information Science and Technology Northwest University China School of Computer Science and Technology Xidian University China

ISBN: (纸本)9781450396899

Sentiment classification is a hot topic in the field of natural language processing. Currently, state-of-the-art classification models follow two steps: pre-training a large language model on upstream tasks, and then using human-labeled data to fine-tune a task-related model. However, there is a large gap between the upstream tasks of the pre-trained model and the downstream tasks being performed, resulting in the need for more labeled data to achieve excellent performance. Manually annotating data is expensive. In this paper, we propose a few-shot sentiment classification method based on Prompt and Contrastive Learning (PCL), which can significantly improve the performance of large-scale pre-trained language models in low-data and high-data regimes. Prompt learning aims to alleviate the gap between upstream and downstream tasks, and the contrastive learning is designed to capture the inter-class and intra-class distribution patterns of labeled data. Thanks to the integration of the two strategies, PCL markedly exceeds baselines with low resources. Extensive experiments on three datasets show that our method has outstanding performance in the few-shot settings. © 2022 ACM.

关键词： Computational linguistics

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A novel image security protection method based on DCT compression theory and hyper-chaotic mapping

A novel image security protection method based on DCT compre...

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2021 International Conference on Information technology and Mechanical Engineering, ITME 2021

作者： Xue, Xianglian Jin, Haiyan School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710077 China Shaanxi University of Chinese Medicine Shaanxi Xianyang712046 China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an University of Technology Shaanxi Xi’an710077 China

This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Cryptography

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Research on liquid crystal display technology based on regional dynamic dimming algorithm 9

Research on liquid crystal display technology based on regio...

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

作者： Yu, Zhitao Sun, Peng Zhou, Mingle Liu, Qianlong Zhao, Shilong Hisense Video Technology Co. Ltd. Qing Dao China Hisense Visual Technology Co. Ltd. Qing Dao China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In vehicle liquid crystal display (LCD) technology has attracted much attention for its wide range of applications in automotive infotainment systems. However, conventional LCD technologies have limitations in terms of energy consumption and display quality, especially in the context of the increasing demand for high contrast and low energy consumption. To address these challenges, this paper carries out a study of LCD technology based on an area dynamic dimming algorithm. The technology significantly improves the contrast ratio of LCDs and effectively reduces energy consumption by dividing the backlight module into multiple independently adjustable regions and dynamically adjusting the backlight brightness of each region according to the image content. The results show that the region dynamic dimming algorithm proposed in this paper can effectively adjust the brightness of the backlight according to the image content, achieving higher display contrast and lower energy consumption. © 2024 IEEE.

关键词： Liquid crystal displays

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