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data Processing Technology for Network Abnormal Traffic Detection 4

Data Processing Technology for Network Abnormal Traffic Dete...

引用

4th international conference on machine learning and Big data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Wang, Kun Fu, Yu Duan, Xueyuan Xu, Jianqiao Liu, Taotao Department of Information Security Naval University of Engineering Wuhan430033 China School of Mathematics and Information Engineering Xinyang Vocational and Technical College Xinyang464000 China College of Computer and Information Technology Xinyang Normal University Xinyang464000 China Henan Key Laboratory of Analysis and Applications of Education Big Data Xinyang Normal University Xinyang464000 China

At present, the threats to network security are also increasing, among which abnormal traffic detection is the key link to ensure network security. Traditional detection methods based on signature or threshold are often difficult to adapt to the increasingly complex network environment and new attack methods. therefore, this paper optimizes and improves the data processing technology, proposes a network ATD method based on particle swarm optimization(PSO) algorithm, and explores in detail the traffic data collection and pre-processing, the feature recognition of abnormal traffic, the application of PSO algorithm, real-time monitoring and response mechanism. the results of two sets of simulation experiments are as follows: compared with the traditional model, the accuracy rate of ATD of the improved algorithm is increased by 7.2% on average, and the detection time is reduced by 7.35s on average. this method not only enhances the adaptability of the model to new attacks, but also improves the degree of automation of detection. © 2024 the Authors.

关键词： Particle swarm optimization (PSO)

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Stop Overkilling Simple Tasks with Black-Box Models, Use More Transparent Models Instead 4th

Stop Overkilling Simple Tasks with Black-Box Models, Use Mo...

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4th international conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Rizzo, Matteo Marcuzzo, Matteo Zangari, Alessandro Schiavinato, Michele Albarelli, Andrea Gasparetto, Andrea Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice VE Mestre30172 Italy

ISBN: (纸本)9789819787012

the ability of deep learning-based approaches to extract features autonomously from raw data while outperforming traditional methods has led to several breakthroughs in artificial intelligence. However, these models suffer from intrinsic opacity, making it difficult to explain their predictions. this is problematic not only because it hinders debugging but, most importantly, because it negatively affects the perceived trustworthiness of the systems. What is often overlooked is that many relatively simple tasks can be solved efficiently and effectively with data processing strategies paired with traditional models that are inherently more transparent. this work highlights the frequently neglected perspective of using knowledge-based and explainability-driven problem-solving in ML. We introduce a set of guidelines to design explainable models and apply them to the task of classifying the ripeness of banana crates. this is done by planning explainability and model design together. We showcase how the task can be solved using opaque deep learning models and more transparent strategies. Notably, there is a minimal loss of accuracy but a significant gain in explainability, which is truthful to the model’s inner workings. Finally, we perform a user study to evaluate the perception of explainability by the end users and discuss our findings. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive learning

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SSDL: Self-Supervised Domain learning for Improved Face recognition 25

SSDL: Self-Supervised Domain Learning for Improved Face Reco...

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25th international conference on pattern recognition (ICPR)

作者： Arachchilage, Samadhi Wickrama Izquierdo, Ebroul Queen Mary Univ London Multimedia & Vis Grp Sch Elect Engn & Comp Sci London England

ISBN: (纸本)9781728188089

Face recognition in unconstrained environments is challenging due to variations in illumination, quality of sensing, motion blur and etc. An individual's face appearance can vary drastically under different conditions creating a gap between train (source) and varying test (target) data. the domain gap could cause decreased performance levels in direct knowledge transfer from source to target. Despite fine-tuning with domain specific data could be an effective solution, collecting and annotating data for all domains is extremely expensive. To this end, we propose a self-supervised domain learning (SSDL) scheme that trains on triplets mined from unlabelled data. A key factor in effective discriminative learning, is selecting informative triplets. Building on most confident predictions, we follow an "easyto-hard" scheme of alternate triplet mining and self-learning. Comprehensive experiments on four different benchmarks show that SSDL generalizes well on different domains.

关键词： Knowledge management

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Analyzing and Comparing Results for Multiple machine learning Models 4

Analyzing and Comparing Results for Multiple Machine Learnin...

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2023 4th international conference on Computer Engineering and Intelligent Control, ICCEIC 2023

作者： Ng, Jefferson Li, Yijian Ji, Xuxiao Yang, Can Wan, Jiaqiao Lu, Xingyu University of California Department of Mechanical Engineering Irvine92697 United States Courant Institute of Mathematical Sciences New York University NY10012 United States University of Waterloo School of Computer Science WaterlooCAN2L 3G1 United States Oregon State University Department of Electrical Engineering and Computer Science Corvallis97331 United States Zhengzhou University Software College Zhengzhou450000 China United World College Changshu China Jiangsu215500 China

ISBN: (纸本)9798350308877

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. this paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance. © 2023 IEEE.

关键词： Convolutional neural networks

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4th international conference on Computer and Communication Technologies, IC3T 2022

4th International Conference on Computer and Communication T...

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4th international conference on Computer and Communication Technologies, IC3T 2022

ISBN: (纸本)9789811985621

the proceedings contain 63 papers. the special focus in this conference is on Computer and Communication Technologies. the topics include: Study of Security for Edge Detection Based Image Steganography;fake Face Image Classification by Blending the Scalable Convolution Network and Hierarchical Vision Transformer;performance Analysis of Osteoarthritis from Knee Radiographs Using Convolutional Neural Networks;Efficient Motion Detection and Compensation Using FPGA;Discover Crypto-Jacker from Blockchain Using AFS Method;Automated Detection for Muscle Disease Using EMG Signal;drowsiness Detection for Automotive Drivers in Real-Time;prediction of Dementia Using Deep learning;performance Analysis of Universal Filtered Multicarrier Waveform with Various Design Parameters for 5G and Beyond Wireless Networks;Shuffled Frog Leap and Ant Lion Optimization for Intrusion Detection in IoT-Based WSN;Diabetic Retinopathy (DR) Detection Using Deep learning;comparative Analysis Using data mining Techniques to Predict the Air Quality and their Impact on Environment;Complexity Reduction by Signal Passing Technique in MIMO Decoders;A New Approach to Improve Reliability in UART Using Checksum Algorithm;Modified VHDL Implementation of 128-Bit Rijndael AES Algorithm by Asymmetric Keys;a Computationally Inexpensive Method Based on Transfer learning for Mobile Malware Detection;a Statistical Approach for Extractive Hindi Text Summarization Using machine Translation;Semantic Parser Using a Sequence-to-Sequence RNN Model to Generate Logical Forms;NFF: A Novel Nested Feature Fusion Method for Efficient and Early Detection of Colorectal Carcinoma;Arrhythmia Classification Using BiLSTM with DTCWT and MFCC Features;A Comprehensive Alert System Based on Social Distancing for Cautioning People Amidst the COVID-19 Pandemic Using Deep Neural Network Models;CNN-Based Breast Cancer Detection.

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Multi-speaker Emotional Speech Synthesis Based on Self-Supervised Speech Representations

Multi-speaker Emotional Speech Synthesis Based on Self-Super...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Mingming Yin Zhihua Huang Kexin Lu Haiyang Zhou School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing School of Computer Science and Technology Xinjiang University Urumqi China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

In recent years, emotional speech synthesis has shown considerable progress. However, some existing emotional speech synthesis methods only model emotion from a single scale, resulting in only global or average emotional expression, unable to synthesize significant emotional speech. In this paper, we propose a multi-scale multi-speaker emotional speech synthesis method based on self-supervised speech model HuBERT. Firstly, the emotion information is extracted from the feature extractor HuBERT. Secondly, the extracted emotion information is modeled by coarse-grained emotion modeling and fine-grained emotion modeling respectively to obtain utterance-level emotion embedding and phoneme-level emotion embedding. through subjective and objective evaluation, we verify that the method is significantly better than the comparison model in terms of speech quality and emotional expression.

关键词： Speech recognition machine learning Feature extraction Speech synthesis data mining

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A Comprehensive Study of Road Traffic Accidents: Hotspot Analysis and Severity Prediction Using machine learning 4

A Comprehensive Study of Road Traffic Accidents: Hotspot Ana...

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4th IEEE Bombay Section Signature conference, IBSSC 2022

作者： Gupta, Utkarsh Varun, M.K. Srinivasa, Gowri Pes University Dept. of Computer Science and Engineering Bengaluru India Pes University Pes Center for Pattern Recognition Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665492911

this study analyses road traffic accident data recorded over a period of time to gain insights to the underlying pain points in the infrastructure and policies. Such insight allows us to focus our efforts in the right direction to make the lives of people safer. the data includes various geographical and meteorological factors affecting the severity of these accidents. We use Kernel density estimation (KDE) plots to analyse hotspots of accident-prone areas weighed against severity over years to understand the evolution of these dangerous zones. Furthermore, we use machine learning algorithms to predict the accident severity given certain parameters and to understand the factors that have a major influence on the severity of the accident. We have studied a publicly available dataset of road traffic accidents in the UK as a proof of concept of the pipeline to understand the underlying patterns of accidents occurring in a region of interest. © 2022 IEEE.

关键词： machine learning

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dataset Balancing Techniques and Supervised learning Algorithms for Predictive Analysis of Rice and Corn Yields 4th

Dataset Balancing Techniques and Supervised Learning Algori...

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4th international conference on Innovations in Computational Intelligence and Computer Vision, ICICV 2024

作者： Di Mauro, Lorenzo Vocaturo, Eugenio Zumpano, Ester CNR-NANOTEC National Research Council Rende Italy DESF—Department of Economic Statistics and Finance University of Calabria Rende Italy DIMES—Department of Computer Engineering Modeling Electronics and Systems University of Calabria Rende Italy

ISBN: (纸本)9789819769919

Agriculture plays a fundamental role in the global socio-economic context, forming the pillar of communities and economies around the world. this study focuses on the impact of pesticides and fertilizers on the crop yield of rice and maize in India, employing supervised machine learning algorithms. the methodology begins with the understanding and pre-processing of an agricultural yield dataset, with a focus on dataset balancing to handle any imbalances in the target variable categories. Several data mining models are then implemented, and selected based on various performance measures. the final stage involves the prediction of crop yield in relation to pesticide and fertilizer levels, followed by the interpretation of the results. In summary, the work provides a starting point for the analysis of agricultural yield in varied geographic settings with diversity in climate and agricultural practices. the methodologies proposed in this study provide a useful framework for future research on agricultural yield optimization solutions. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Self-supervised learning

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4th international conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

4th International Conference on Artificial Intelligence in H...

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4th international conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

ISBN: (纸本)9783031358937

the proceedings contain 85 papers. the special focus in this conference is on Artificial Intelligence in HCI. the topics include: Unvoiced Vowel recognition Using Active Bio-Acoustic Sensing for Silent Speech Interaction;Is It Possible to Measure Real-Time VR Motion Sickness with EDA Wavelet Features?;How Do AI Explanations Affect Human-AI Trust?;the thousand Faces of Explainable AI Along the machine learning Life Cycle: Industrial Reality and Current State of Research;how to Explain It to a Model Manager?: A Qualitative User Study About Understandability, Trustworthiness, Actionability, and Action Efficacy;application of Reinforcement learning for Intelligent Support Decision System: A Paradigm Towards Safety and Explainability;INFEATURE: An Interactive Feature-Based-Explanation Framework for Non-technical Users;A Quantitative Comparison of Causality and Feature Relevance via Explainable AI (XAI) for Robust, and Trustworthy Artificial Reasoning Systems;Evaluating Explainable AI (XAI) in Terms of User Gender and Educational Background;AI Explainability, Interpretability, Fairness, and Privacy: An Integrative Review of Reviews;examining Simulated Agricultural Tasks Using an Arm-Support Exoskeleton;Emotional Debiasing Explanations for Decisions in HCI;exploring Mental Models for Explainable Artificial Intelligence: Engaging Cross-disciplinary Teams Using a Design thinking Approach;requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work;Exploring the Effect of Visual-Based Subliminal Persuasion in Public Speeches Using Explainable AI Techniques;Assessing the Impact of Cognitive Biases in AI Project Development;How is the AI Perceived When It Behaves (Un)Fairly?;Distance Correlation GAN: Fair Tabular data Generation with Generative Adversarial Networks;through a Fair Looking-Glass: Mitigating Bias in Image datasets;Ethical AI Based Decision Making to Reduce Tax Related Debts for Governments;improving Fairness via Deep Ensemble Fra

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4th international conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

4th International Conference on Artificial Intelligence in H...

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4th international conference on Artificial Intelligence in HCI, AI-HCI 2023, held as part of the 25th international conference on Human-Computer Interaction, HCII 2023

ISBN: (纸本)9783031358906

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