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Mimic Defense Structured information System Threat Identification and Centralized Control

Journal of Physics: Conference Series 2019年第3期1187卷

作者： Bo Zhang Weichao Li Xin Sun Yufeng Zhao State Grid Key Laboratory of Information & Network Security Nanjing China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China State Grid Zhejiang Electric Power Research Institute Hangzhou China

With the speeding up of the informatization process of our national economy and social development, the degree of informatization in our country is getting higher and higher. information system has become the key infrastructure of our country. The security of these basic information networks and important information systems has been seriously related to national security and social stability, to the collective interests of enterprises, and to the vital interests of the broad masses of the people. Increasing application in improving work efficiency and economic benefits at the same time, the increasingly com-plex information system has also brought greater challenges to operation and maintenance managers. The use of known or unknown vulnerabilities, causing damage to information systems, stealing sensitive information, endangering the normal operation of business systems and other behaviors, has become one of the main causes of various security incidents. How to effectively deal with the vulnerability threat and various vulnerability problems of enterprise information systems, concentrate on the implementation of information security vulnerability warning related risks, and provide solutions to related vulnerability threats, have become an urgent problem to be solved. Therefore, we construct the mimic structured enterprise information system by establishing the threat identification system through unified management to form a comprehensive and secure cyber threat management system.

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Rapid Maize Seedling Detection Based on Receptive-Field and Cross-Dimensional information Interaction

Rapid Maize Seedling Detection Based on Receptive-Field and ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Fengqi Hao Shulei Zhu Dexin Ma Xiangjun Dong Hoiio Kong Xia Liu Chunhua Mu 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 Faculty of Data Science City University of Macau Macau China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Communication College Qingdao Agricultural University Qingdao China National Engineering Laboratory of Wheat and Maize/Key Laboratory of Biology and Genetic Improvement of Maize in Northern Yellow-huai River Plain Ministry of Agriculture Maize Research Institute Shandong Academy of Agricultural Sciences Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The seedling stage is important in the growth and development of maize, and it is also a critical period that affects maize yield and quality. Accurately recognizing this stage of maize is challenging, as current maize seedling detection methods struggle with small sizes and complex environments. This paper proposes a rapid maize seedling detection method that emphasizes spatial feature extraction in the receptive field and cross-dimensional interaction—receptive field triplet attention YOLO (RFT-YOLO). First, we design a convolutional module for cross-dimensional information interaction within the receptive field. Second, we introduce an advanced selective fusion network, which boosts the multi-scale fusion capability of small object features while reducing the GFLOPs of the model. Additionally, we employ Inner-IoU to diminish sensitivity to positional biases of small objects and expedite the convergence of the loss function. Finally, we create a dataset of maize seedlings to conduct our experiments. Experimental results demonstrate that the RFT-YOLO significantly enhances performance, reducing the number of parameters by 35.3% and GFLOPs by 14.24%, compared to the baseline model. Moreover, the mean average precision (mAP) improves from 89.6% to 91.5%. These improvements confirm the model's effectiveness in detecting maize seedlings.

关键词： YOLO Sensitivity Accuracy Semantics Feature extraction Complexity theory Data mining Cybernetics Convergence

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Global context aware RCNN for object detection

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Xie, Haoyu Zhu, Mai Tie, Ming Chen, Junxin School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Science and Technology on Space Physics Laboratory Beijing100076 China College of Medicine and Biological Information Engineering Northeastern University Shenyang110819 China

RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map. However, these cropped feature maps of local receptive fields will heavily lose global context information. To tackle this problem, we propose a novel end-to-end trainable framework, called Global Context Aware (GCA) RCNN, aiming at assisting the neural network in strengthening the spatial correlation between the background and the foreground by fusing global context information. The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively. Specifically, we leverage the dense connection to improve the information flow of the global context at different stages in the top-down process of FPN, and further use the attention mechanism to refine the global context at each level in the feature pyramid. In the end, we also present a lightweight version of our method, which only slightly increases model complexity and computational burden. Experimental results on COCO benchmark dataset demonstrate the significant advantages of our approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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YOLO-TP: A process detection model of tunnel real scene based on gradient information interaction

YOLO-TP: A process detection model of tunnel real scene base...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Jiaying Fan Shuping Pan Xinbo Jiang Shengtao Zhang Hongliang Liu Chuan Wang Jianghua Li Chenglin Ke 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 Technological Innovation Department Shandong Hi-Speed Group Co Ltd JiNan China School of Qilu Transportation Shandong University JiNan China China Railway 14th Bureau Group First Engineering Development Co. Ltd RiZhao China

ISBN: (数字)9798331533991

ISBN: (纸本)9798331534004

Target detection technology is an effective method to detect tunnel operation safety. Although the field has made some progress in detection models, the lack of datasets and detection models has hindered its development. We produce and propose Tunnel Processes data (TP), a challenging dataset of high-precision real-world tunnel process target detection that reduces the limitations of research in this field. TP has a high resolution: 1920×1080, 1734images,9 categories. TP is the first tunnel process data set under construction to date, and its fidelity exceeds that of other tunnel process datasets in the field of target detection. In addition, we also propose a YOLO-TP benchmark model to make up for the lack of a high-precision tunnel process image target detection baseline model. Experiments on TP datasets demonstrate their validity, and experiments on the dataset presented in this paper demonstrate the validity of the benchmark model.

关键词： Accuracy Image resolution Computational modeling Object detection Benchmark testing Big Data Feature extraction Data models Safety Artificial intelligence

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SCARE of Secret Ciphers under Rough Leakage Model

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security and Communication networks 2023年第1期2023卷

作者： Wang, Mengxing Tang, Ming Zhang, Xige Chen, Qi Ding, Haili Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Hubei Wuhan430000 China Beijing Smartchip Microelectronics Technology Co. Ltd. Beijing100192 China Marketing Service Center State Grid Electric Power Co. Ltd. Ningxia750002 China

Most previousside-channel analysis-based reverse engineering (SCARE) methods were based on collision attack. However, the collision-based methods generally need noise-free traces and can hardly be verified by realistic implementations. This paper proposes a novel SCARE, which is named as OSHDG-SCARE, targeting on secret S-box. We define a graph as OSHDG (one-step HD graph) to recover the parameters of any secret S-box. Due to the redundancy of OSHDG, the multiple paths can filter the correct results and improve the success rate of the reverse method even when the samples deviate from leakage model. We classify the deviation of side channel signal as two types, which are the gap between real leakage and leakage model, and measurement noise. Experiments are performed on real power traces satisfying the inaccurate HW (Hamming Weight) leakage model from a software implementation of AES-like cipher. OSHDG-SCARE recovers the secret S-box when the deviation rate of traces is 1.45%. © 2023 Mengxing Wang et al.

关键词： Side channel attack

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An edge enhancement-based fusion method of infrared and visible images in low light

An edge enhancement-based fusion method of infrared and visi...

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Signal, information and Data Processing (ICSIDP), IEEE International Conference on

作者： Xiang Gao Xiyan Wang Hanbin Bao Qian Li Guohua Lv Faculty of Comupter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Bresee Technology Co. Ltd. Jinan China Shandong Grain Group Co. Ltd. Jinan China 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

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The combination of infrared and visible images can result in the generation of a single image with a high degree of detail and important targets in a variety of scenes, which is beneficial for human recognition. However, in low-light environments, low-intensity visible images tend to blur valuable information, which is often overlooked. This results in the loss of important information. While existing methods address the issue of low illumination and highlight the illumination, a considerable amount of structural information is lost when adjusting illumination, resulting in the omission of texture details. To solve aforementioned problems, we proposes a method for edge enhancement in low-illumination scenes. This approach aims to minimise the loss of edge information while simultaneously enhancing the illumination, thereby producing fusion images with enhanced texture detail. The initial step involves the design of an illumination enhancement module, which is employed to segregate the compromised illumination present from the scene information. Given the fact that image edge information has the capacity for effective structural modeling, a low-light visible image edge extraction network is designed to model structural information, which can accurately highlight important structural information and insert it in fused images. The results produced by this method have better visual perception and minimize structural loss. Extensive experiments demonstrate that the method outperforms SOTA methods in term of visual quality and quantitative metrics.

关键词： Reflectivity Measurement Visualization Target recognition Image edge detection Lighting Data mining Image reconstruction Image fusion Visual perception

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Social-enhanced recommendation using graph-based contrastive learning

Social-enhanced recommendation using graph-based contrastive...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Peng Xue Qian Gao Jun Fan 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 Shandong 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 Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

The social network-based recommendation model use social network information to mitigate data sparsity issues and improve the accuracy of recommendation models. However, In most social network-based recommendation algorithms, the social neighbors' contributions are difficult to distinguish from the central user's, neglecting hidden correlations in social infor-mation. In order to solve this problem, this paper presents a graph-based contrastive learning framework for social-enhanced recommendation (SoGCLR) that utilizes implicit social information captured by a new social relation attention mechanism, en-rich user representations, and improve model robustness through graph-based contrastive learning. Specifically, the paper captures the degree of contribution of each neighbor in the social graph to the central user through a social relation attention layer, thus obtaining hidden correlations in social information, and further integrates this with user information in the user-item interaction graph to enrich user representations. In addition, the paper incorporates graph-based contrastive learning into the recommendation task using cross-layer contrastive learning, calculating contrastive loss and mapping nodes with similar but different exposure rates to nearby regions to mitigate exposure bias issues. Results from Ciao and Epinions demonstrate that SoGCLR reduces RMSE and MAE by 1.33% to 1.84% compared with baseline models.

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LI-FPN: Depression and Anxiety Detection from Learning and Imitation

LI-FPN: Depression and Anxiety Detection from Learning and I...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang 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 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 Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM Dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM Dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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Multi-Channel Hypergraph Convolution Group Recommendation with Member information Enhancement

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan 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 Shandong 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 Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach.

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IDFusion: An Infrared and Visible Image Fusion network for Illuminating Darkness

IDFusion: An Infrared and Visible Image Fusion Network for I...

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International Conference on Computer Supported Cooperative Work in Design

作者： Guohua Lv Xiyan Wang Zhonghe Wei Jinyong Cheng Guangxiao Ma Hanju Bao 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 Faculty of Comuter 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 College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Lianxin Intelligent Technology (Shandong) Co. Ltd Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The purpose of infrared and visible image fusion is to combine the background information of the visible images and the thermal target information of the infrared images. Current fusion methods often neglect the challenges of low-light conditions. In the night scene, the existing methods fail to capture texture information from the visible images that is hidden in the darkness, producing suboptimal fusion results that can hinder subsequent visual applications. Therefore, we propose a method for infrared and visible image fusion under night scenes, termed as IDFusion. Specifically, our method is divided into three parts, first, a dense auto-encoder is designed to obtain more useful features from the source images. Then we design a brightness enhancement network that removes visible degraded illumination maps to obtain brightness-enhanced features. Finally, a texture fusion network is designed so that the infrared features and enhanced visible features avoid texture loss during the fusion process. Experimental results show that our network can obtain better fusion results, outperforming state-of-the-art methods in terms of subjective visual effects and quantitative metrics.

关键词： Measurement Federated learning Brightness Lighting Visual effects Image fusion

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