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Artificial Immune Detection for Network Intrusion Data Based on Quantitative Matching Method

Computers, Materials & Continua 2024年第2期78卷 2361-2389页

作者： CaiMing Liu Yan Zhang Zhihui Hu Chunming Xie School of Electronic Information and Artificial Intelligence Leshan Normal UniversityLeshan614000China Intelligent Network Security Detection and Evaluation Laboratory Leshan Normal UniversityLeshan614000China Internet Natural Language Intelligent Processing Key Laboratory of Education Department of Sichuan Province Leshan Normal UniversityLeshan614000China

Artificial immune detection can be used to detect network intrusions in an adaptive approach and proper matching methods can improve the accuracy of immune detection *** paper proposes an artificial immune detection model for network intrusion data based on a quantitative matching *** proposed model defines the detection process by using network data and decimal values to express features and artificial immune mechanisms are simulated to define immune ***,to improve the accuracy of similarity calculation,a quantitative matching method is *** model uses mathematical methods to train and evolve immune elements,increasing the diversity of immune recognition and allowing for the successful detection of unknown *** proposed model’s objective is to accurately identify known intrusions and expand the identification of unknown intrusions through signature detection and immune detection,overcoming the disadvantages of traditional *** experiment results show that the proposed model can detect intrusions *** has a detection rate of more than 99.6%on average and a false alarm rate of 0.0264%.It outperforms existing immune intrusion detection methods in terms of comprehensive detection performance.

关键词： Immune detection network intrusion network data signature detection quantitative matching method

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A Novel Unsupervised Feature Selection based on Correlation Measure between Heterogeneous Features 6

A Novel Unsupervised Feature Selection based on Correlation ...

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6th IEEE International Conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Cai, Jia-Ming Jia, Hong Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9798350325485

Most existing feature selection algorithms only focus on a certain type of data, either numerical or categorical. In this paper, we present an unsupervised heterogeneous feature selection method based on a novel feature correlation measurement. Firstly, a unified correlation measuring framework is designed to quantify the correlations between any two features, including features with the same type and different types. Then, a filter-based feature selection algorithm is proposed to remove redundant features and obtain a best feature subset. Various experiments have been conducted on different mixed data sets and the results indicate that the proposed method can obtain better clustering performance with a smaller heterogeneous feature subset. © 2023 IEEE.

关键词： Feature Selection

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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Computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and Technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Face Forgery Detection Algorithm Based on Improved MobileViT Network 8

Face Forgery Detection Algorithm Based on Improved MobileViT...

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8th International Conference on intelligent Computing and Signal processing, ICSP 2023

作者： Wang, Tiantian Lu, Xiaoqi Inner Mongolia University of Science and Technology School of Information Engineering Baotou014010 China Inner Mongolia Key Laboratory of Pattern Recognition and Intelligent Image Processing Baotou014010 China Inner Mongolia University of Technology Institute of Information Engineering Hohhot010051 China

ISBN: (纸本)9798350302455

DeepFakes blur the boundaries between reality and forgery, resulting in the collapse of exiting credit system, causing immeasurable consequences for national security and social order. Through analysis of existing face forgery techniques, it is found that most generation techniques rely on random noise distribution, and global information will be lost after up sampling. Therefore, we propose a deepfake detection algorithm based on improved MobileViT, which uses CNN local space biasing and the global space representation of the Transformer network to learn the local features and global representation of forged faces, respectively. Coordinate attention is introduced to obtain directional perception and position sensitive information, making the model locate synthetic traces of fake faces better and fusion local and global representation more effectively. For the improved generalization of the model, with the GELU activation function to solve the problem of neuron death. Our model achieved 96.2% on FF++(C23) datasets, and 93.7%,94.1%,96.3%,87.9% on DF, F2F, FS, and NT datasets, respectively. Comparing with previous methods, our model has shown detection robustness and better generalization. © 2023 IEEE.

关键词： National security

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A knowledge-enhanced interest segment division attention network for click-through rate prediction

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Neural Computing and Applications 2024年第34期36卷 21817-21837页

作者： Liu, Zhanghui Chen, Shijie Chen, Yuzhong Su, Jieyang Zhong, Jiayuan Dong, Chen College of Computer and Data Science Fuzhou University Fujian Province Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fujian Province Fuzhou350108 China

Click-through rate (CTR) prediction aims to estimate the probability of a user clicking on a particular item, making it one of the core tasks in various recommendation platforms. In such systems, user behavior data are crucial for capturing user interests, which has garnered significant attention from both academia and industry, leading to the development of various user behavior modeling methods. However, existing models still face unresolved issues, as they fail to capture the complex diversity of user interests at the semantic level, refine user interests effectively, and uncover users’ potential interests. To address these challenges, we propose a novel model called knowledge-enhanced Interest segment division attention network (KISDAN), which can effectively and comprehensively model user interests. Specifically, to leverage the semantic information within user behavior sequences, we employ the structure of a knowledge graph to divide user behavior sequence into multiple interest segments. To provide a comprehensive representation of user interests, we further categorize user interests into strong and weak interests. By leveraging both the knowledge graph and the item co-occurrence graph, we explore users’ potential interests from two perspectives. This methodology allows KISDAN to better understand the diversity of user interests. Finally, we extensively evaluate KISDAN on three benchmark datasets, and the experimental results consistently demonstrate that the KISDAN model outperforms state-of-the-art models across various evaluation metrics, which validates the effectiveness and superiority of KISDAN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Contrastive Learning

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An Enhanced Particle Swarm Algorithm Based on Fitness Landscape information 13

An Enhanced Particle Swarm Algorithm Based on Fitness Landsc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Diao, Zhenya Yu, Fei Guan, Jian Liu, Meitong School of Physics and Information Engineering Minnan Normal University Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China School of Physics and Information Engineering Minnan Normal University Key Laboratory of Light Field Manipulation and System Integration Applications in Fujian Province Key Lab of Intelligent Optimization and Information Processing Zhangzhou36300 China

ISBN: (纸本)9798350308365

In the original Particle Swarm Optimization (PSO), the selection of parameters significantly impacts the search capability of PSO. The paper introduces an innovative algorithm called Fitness Landscape-based PSO (FLPSO) to address the impact of parameter selection on the search capability of PSO. The algorithm focuses on acquiring fitness landscape information and analyzing the population state to intelligently select appropriate parameters in each generation. FLPSO defines four primary states: exploration, exploitation, convergence, and escaping state. In the convergence state, an elite learning strategy is incorporated to help the algorithm escape potential local optima by targeting the global best particle. To validate the efficacy of FLPSO, the paper conducted experiments on 30 different characteristic test functions from CEC2017. FLPSO was compared with six existing algorithms: PSO, PSO-TVAC, APSO, AWPSO, RPSO and ASRPPSO. The experimental results showed significant improvements in FLPSO, providing promising evidence for its effectiveness. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Dual-Branch Attention Transformer for Visual Commonsense Reasoning 6

Dual-Branch Attention Transformer for Visual Commonsense Rea...

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6th International Conference on Frontier Technologies of information and Computer, ICFTIC 2024

作者： Ma, Xuerui Bai, Zongwen Zhou, Meili Gao, Yiqun School of Physics and Electronic Information Yan'an University Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan'an China

ISBN: (纸本)9798331541750

Visual Commonsense Reasoning (VCR) is a challenging task that requires a model to select the correct answer in the context of a given image and question, while also providing a reasonable explanation to justify the chosen answer. In this paper, we propose a novel dual-branch attention network model that effectively models dense intra-modal and inter-modal interactions. Our model utilizes a self-attention mechanism to attend to visual and textual information independently, while employing a bidirectional guided attention mechanism to facilitate effective interaction between different modalities, thus correlating visual information with textual information. By stacking multiple dual-branch attention modules (DBAs), our model is capable of deeply exploring data features and extracting more powerful and informative prior features. By achieving a common focus on visual and textual information, we enhance the subtlety of our understanding and significantly improve our ability to perform visual reasoning. Our comprehensive and qualitative assessments on the public VCR dataset illustrate that our methodology surpasses most models, providing a new solution for Visual Commonsense Reasoning tasks. © 2024 IEEE.

关键词： dual-branch attention multimodel Visual Commonsense Reasoning

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Tibetan Question Intention Classification Based on Feature Perception of Question Words 4

Tibetan Question Intention Classification Based on Feature P...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Quecairang, Guan Maojia, Yang Te, Rou Chajia, Se Zhenjiacuo, Ci Qinghai Normal University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Xining China

ISBN: (纸本)9798331531225

Question intent classification is an important part of the Tibetan Q&A system. It is a challenging task to use deep learning methods to classify the intent of Tibetan questions. This paper aims to: (1) The traditional Tibetan question intent classification scheme is too coarse, which is not conducive to the problem intent recognition task in the question answering system. (2) The Tibetan question intent type is highly dependent on the local spatial characteristics with interrogative virtual words as the core, which leads to the problem of poor classification effect, and a novel Tibetan question intent classification scheme is proposed, and each question intent type is defined. On this basis, through a detailed analysis of the semantic features and writing rules of Tibetan interrogators, a semantic information labeling algorithm for Tibetan interrogators was designed, and an BERT_RCNN intentional classification model for Tibetan questions was trained by combining the advantages of Tibetan Pre-Training (TiBERT) and recurrent convolutional neural network. The F1 value of this method can reach 94.87% on the test set, which can accurately identify user needs, compress the search space and optimize model decision-making, and significantly improve the performance of the Tibetan Q&A system. © 2024 IEEE.

关键词： Question answering

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GaborNeXt: A Low-level Visual Area Encoding Model Based on Convolution Kernel Optimization- Improvement of Gabornet 16

GaborNeXt: A Low-level Visual Area Encoding Model Based on C...

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16th International Conference on Digital Image processing, ICDIP 2024

作者： Zhao, Weichen Wang, Linyuan Ma, Shuxiao Hou, Libin Zhang, Chi Yan, Bin Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering University Zhengzhou450001 China

ISBN: (数字)9781510682917

ISBN: (纸本)9781510682900

The Gabor wavelet is widely used to simulate the receptive fields of simple cells in the low-level visual cortex (such as V1,V2 and V3) of the human visual system. Based on this, end-to-end encoding models have achieved advanced encoding results in the low-level visual cortex. However, most current end-to-end encoding models are lightweight models with relatively simple structures and few parameters. This limitation may cause the models to perform poorly in processing detailed features of different frequencies and directions in complex Gabor feature maps. In this paper, a novel visual coding model GaborNeXt based on Gabor features is proposed. The model utilizes ConvNeXt convolutional layers to group independent convolutional kernels for convolutional operations and concatenates the outputs of each group to enhance nonlinear expressive power. We conducted experiments on the NSD (Natural Scenes Dataset) and the results demonstrate that our model outperforms the baseline models in encoding accuracy across several the low-level visual cortex. Additionally, we compared the effects of various Gabor convolutional layer kernel sizes on model performance through ablation experiments and found that using larger convolutional kernels in the Gabor convolutional layer has a positive impact on the model's performance. © 2024 SPIE.

关键词： Deep neural networks

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Unsigned Road Incidents Detection Using Improved RESNET From Driver-View Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1203-1216页

作者： Li, Changping Wang, Bingshu Zheng, Jiangbin Zhang, Yongjun Chen, C.L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature of traffic introduces significant challenges, particularly in detecting sudden and temporary incidents. In this article, we propose a novel detection framework, multihead attention ResNet with dynamic bottleneck (DB_RESNET_MHA), to identify physical unsigned road incidents. Our approach introduces three key innovations. First, we develop a tailored data augmentation strategy to create images that closely mimic the complex variations found in real-world road environments. Second, we enhance model expressiveness by employing attention mechanisms to nonlinearly integrate convolutional kernels within the residual network. Furthermore, we refine the prediction head by applying spatially distinct attention weights, enabling the model to capture intricate correlations between different features more effectively. To demonstrate the effectiveness of our method, we create a dataset for unsigned road incidents (UnsignRI), comprising a total of 16 323 images that capture 12 distinct types of incidents. It stands out as the most comprehensive dataset in the field, encompassing a wide range of geographical features and incident categories. Experimental results show that DB_RESNET_MHA achieves an average accuracy of 96.2% and a f1-score of 0.955 across various categories of unsigned incidents, surpassing other models. © 2020 IEEE.

关键词： Highway accidents

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