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A novel intrusion detection model for the CAN bus packet of in-vehicle network based on attention mechanism and autoencoder

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Digital Communications and Networks 2023年第1期9卷 14-21页

作者： Pengcheng Wei Bo Wang Xiaojun Dai Li Li Fangcheng He School of Mathematics and Information Engineering Chongqing University of EducationChongqingChina School of Automation Chongqing University of Posts and TelecommunicationsChongqingChina Department of Modern Service Chongqing Energy Industry Technician CollegeChongqingChina College of Foreign Languages Literature Chongqing University of EducationChongqingChina

The attacks on in-vehicle Controller Area Network(CAN)bus messages severely disrupt normal communication between vehicles.Therefore,researches on intrusion detection models for CAN have positive business value for vehicle security,and the intrusion detection technology for CAN bus messages can effectively protect the invehicle network from unlawful attacks.Previous machine learning-based models are unable to effectively identify intrusive abnormal messages due to their inherent shortcomings.Hence,to address the shortcomings of the previous machine learning-based intrusion detection technique,we propose a novel method using attention mechanism and AutoEncoder for Intrusion Detection(AMAEID).The AMAEID model first converts the raw hexadecimal message data into binary format to obtain better input.Then the AMAEID model encodes and decodes the binary message data using a multi-layer denoising autoencoder model to obtain a hidden feature representation that can represent the potential features behind the message data at a deeper level.Finally,the AMAEID model uses the attention mechanism and the fully connected layer network to infer whether the message is an abnormal message or not.The experimental results with three evaluation metrics on a real in-vehicle CAN bus message dataset outperform some traditional machine learning algorithms,demonstrating the effectiveness of the AMAEID model.

关键词： Controller area network bus packet In-vehicle network attention mechanism Autoencoder

Prediction of Listed Company Innovation Ability Based on attention mechanism

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Journal of the Operations Research Society of China 2023年第1期11卷 177-187页

作者： Huan-Yu Ma Yan Xu Yu-Long Liu Department of Information and Computer Science University of Science and Technology BeijingBeijing100083China No.15 Research Institute China Electronics Technology Group CorporationBeijing100083China

Innovation is the center of the enterprise development, which is the main driving force of enterprises’ competitiveness. To evaluate the enterprises’ innovation ability, we firstly establish an index system which aims at analyzing the enterprise innovation ability. We also retrieve the data of the listed companies in Wind database from 2015 to 2019 and label them using factor analysis method. Then, a new deep learning classificational framework with attention mechanism and LSTM is established. The results show that when attention mechanism and LSTM are added into the convolutional neural network(CNN), the model’s prediction performance is better improved, and the accuracy, recall, precision and F-score are 0.914, 0.914, 0.916 and 0.915, respectively. This indicates the strong generalization ability of our new model. Finally, we also find that patents and R &D expenditures are the most important factor affecting the corporate innovation ability through SHapley Additive exPlanations(SHAP). Companies with more patents and R &D expenditures are generally considered to be more innovative.

关键词： Corporate innovation ability convolutional neural network attention mechanism long short-term memory

Dense Face Network:A Dense Face Detector Based on Global Context and Visual attention mechanism

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Machine Intelligence Research 2022年第3期19卷 247-256页

作者： Lin Song Jin-Fu Yang Qing-Zhen Shang Ming-Ai Li Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboratory of Computational Intelligence and Intelligent Systems Beijing 100124China

Face detection has achieved tremendous strides thanks to convolutional neural networks. However, dense face detection remains an open challenge due to large face scale variation, tiny faces, and serious occlusion. This paper presents a robust, dense face detector using global context and visual attention mechanisms which can significantly improve detection accuracy. Specifically, a global context fusion module with top-down feedback is proposed to improve the ability to identify tiny faces. Moreover, a visual attention mechanism is employed to solve the problem of occlusion. Experimental results on the public face datasets WIDER FACE and FDDB demonstrate the effectiveness of the proposed method.

关键词： Face detection global context attention mechanism computer vision deep learning

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Event Temporal Relation Extraction with attention mechanism and Graph Neural Network

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Tsinghua Science and Technology 2022年第1期27卷 79-90页

作者： Xiaoliang Xu Tong Gao Yuxiang Wang Xinle Xuan the Department of Computer Science and Engineering Hangzhou Dianzi UniversityHangzhou 310018China the Hangzhou Sanhui Digital Information Technology Co. Ltd.Hangzhou 310018China

Event temporal relation extraction is an important part of natural language processing.Many models are being used in this task with the development of deep learning.However,most of the existing methods cannot accurately obtain the degree of association between different tokens and events,and event-related information cannot be effectively integrated.In this paper,we propose an event information integration model that integrates event information through multilayer bidirectional long short-term memory(Bi-LSTM)and attention mechanism.Although the above scheme can improve the extraction performance,it can still be further optimized.To further improve the performance of the previous scheme,we propose a novel relational graph attention network that incorporates edge attributes.In this approach,we first build a semantic dependency graph through dependency parsing,model a semantic graph that considers the edges’attributes by using top-k attention mechanisms to learn hidden semantic contextual representations,and finally predict event temporal relations.We evaluate proposed models on the TimeBank-Dense dataset.Compared to previous baselines,the Micro-F1 scores obtained by our models improve by 3.9%and 14.5%,respectively.

关键词： temporal relation extraction neural network attention mechanism graph attention network

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Identification and classification of promoters using the attention mechanism based on long short-term memory

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Frontiers of Computer Science 2022年第4期16卷 103-109页

作者： Qingwen Li Lichao Zhang Lei Xu Quan Zou Jin Wu Qingyuan Li Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China State Key Laboratory of Brain and Cognitive Science Institute of BiophysicsChinese Academy of SciencesBeijing100101China Forestry and Fruit Tree Research Institute Wuhan Academy of Agricultural SciencesWuhan430075China School of Intelligent Manufacturing and Equipment Shenzhen Institute of Information TechnologyShenzhen518172China School of Electronic and Communication Engineering Shenzhen PolytechnicShenzhen518055China School of Management Shenzhen PolytechnicShenzhen518055China

A promoter is a short region of DNA that can bind RNA polymerase and initiate gene transcription.It is usually located directly upstream of the transcription initiation site.DNA promoters have been proven to be the main cause of many human diseases,especially diabetes,cancer or Huntington’s disease.Therefore,the classification of promoters has become an interesting problem and has attracted the attention of many researchers in the field of bioinformatics.Various studies have been conducted in order to solve this problem,but their performance still needs further improvement.In this research,we segmented the DNA sequence in a k-mers manner,then trained the word vector model,inputted it into long short-term memory(LSTM)and used the attention mechanism to predict.Our method can achieve 93.45%and 90.59%cross-validation accuracy in the two layers,respectively.Our results are better than others based on the same data set,and provided some ideas for accurately predicting promoters.In addition,this research suggested that natural language processing can play a significant role in biological sequence prediction.

关键词： promoter bioinformatics natural language processing attention mechanism

Building Indoor Dangerous Behavior Recognition Based on LSTM-GCN with attention mechanism

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Computer Modeling in Engineering & Sciences 2023年第11期137卷 1773-1788页

作者： Qingyue Zhao Qiaoyu Gu Zhijun Gao Shipian Shao Xinyuan Zhang School of Management Shenyang Jianzhu UniversityShenyang110168China School of Information and Communication Engineering North University of ChinaTaiyuan030051China School of Information and Control Engineering Shenyang Jianzhu UniversityShenyang110168China

Building indoor dangerous behavior recognition is a specific application in the field of abnormal human recognition.A human dangerous behavior recognition method based on LSTM-GCN with attention mechanism(GLA)model was proposed aiming at the problem that the existing human skeleton-based action recognition methods cannot fully extract the temporal and spatial features.The network connects GCN and LSTMnetwork in series,and inputs the skeleton sequence extracted by GCN that contains spatial information into the LSTM layer for time sequence feature extraction,which fully excavates the temporal and spatial features of the skeleton sequence.Finally,an attention layer is designed to enhance the features of key bone points,and Softmax is used to classify and identify dangerous behaviors.The dangerous behavior datasets are derived from NTU-RGB+D and Kinetics data sets.Experimental results show that the proposed method can effectively identify some dangerous behaviors in the building,and its accuracy is higher than those of other similar methods.

关键词： Human skeleton building indoor dangerous behaviors recognition graph convolution network long short term memory network attention mechanism

Bilateral U-Net semantic segmentation with spatial attention mechanism

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CAAI Transactions on Intelligence Technology 2023年第2期8卷 297-307页

作者： Guangzhe Zhao Yimeng Zhang Maoning Ge Min Yu Beijing University of Civil Engineering and Architecture College of Electrical and Information EngineeringBeijingChina Graduate School of Informatics Nagoya UniversityNagoyaJapan Department of General Surgery Guangdong Provincial People's HospitalGuangdong Academy of Medical SciencesGuangzhouChina

Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small-scale samples,a bilateral U-Net network model with a spatial attention mechanism is designed.The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial attention(APSA)module compared to the Attenuated Spatial Pyramid module,which can increase the receptive field and enhance the information,and finally adds the context fusion prediction branch that fuses high-semantic and low-semantic prediction results,and the model effectively improves the segmentation accuracy of small data sets.The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks,the algorithm has a better segmentation effect and segmentation accuracy,and its mIOU reaches 75.85%.Moreover,to verify the generality of the model and the effectiveness of the APSA module,experiments were conducted on the VOC 2012 data set,and the APSA module improved mIOU by about 12.2%.

关键词： attention mechanism receptive field semantic fusion semantic segmentation spatial attention module U-Net

AF-Net:A Medical Image Segmentation Network Based on attention mechanism and Feature Fusion

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Computers, Materials & Continua 2021年第11期69卷 1877-1891页

作者： Guimin Hou Jiaohua Qin Xuyu Xiang Yun Tan Neal N.Xiong College of Computer Science and Information Technology Central South University of Forestry&TechnologyChangsha410004China Department of Mathematics and Computer Science Northeastern State UniversityTahlequah74464OKUSA

Medical image segmentation is an important application field of computer vision in medical image processing.Due to the close location and high similarity of different organs in medical images,the current segmentation algorithms have problems with mis-segmentation and poor edge segmentation.To address these challenges,we propose a medical image segmentation network(AF-Net)based on attention mechanism and feature fusion,which can effectively capture global information while focusing the network on the object area.In this approach,we add dual attention blocks(DA-block)to the backbone network,which comprises parallel channels and spatial attention branches,to adaptively calibrate and weigh features.Secondly,the multi-scale feature fusion block(MFF-block)is proposed to obtain feature maps of different receptive domains and get multi-scale information with less computational consumption.Finally,to restore the locations and shapes of organs,we adopt the global feature fusion blocks(GFF-block)to fuse high-level and low-level information,which can obtain accurate pixel positioning.We evaluate our method on multiple datasets(the aorta and lungs dataset),and the experimental results achieve 94.0%in mIoU and 96.3%in DICE,showing that our approach performs better than U-Net and other state-of-art methods.

关键词： Deep learning medical image segmentation feature fusion attention mechanism

Narrow Pooling Clothing Classification Based on attention mechanism

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Journal of Donghua University(English Edition) 2022年第4期39卷 367-372页

作者：马骁王绍宇叶少萍樊静宜徐安夏小玲 School of Computer Science and Technology Donghua UniversityShanghai 201620China

In recent years,with the rapid development of e-commerce,people need to classify the wide variety and a large number of clothing images appearing on e-commerce platforms.In order to solve the problems of long time consumption and unsatisfactory classification accuracy arising from the classification of a large number of clothing images,researchers have begun to exploit deep learning techniques instead of traditional learning methods.The paper explores the use of convolutional neural networks(CNNs)for feature learning to enhance global feature information interactions by adding an improved hybrid attention mechanism(HAM)that fully utilizes feature weights in three dimensions:channel,height,and width.Moreover,the improved pooling layer not only captures local feature information,but also fuses global and local information to improve the misclassification problem that occurs between similar categories.Experiments on the Fashion-MNIST and DeepFashion datasets show that the proposed method significantly improves the accuracy of clothing classification(93.62%and 67.9%)compared with residual network(ResNet)and convolutional block attention module(CBAM).

关键词： clothing classification convolutional neural network(CNN) residual network(ResNet) attention mechanism narrow pooling

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