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6th international conference on Security and Privacy in New computing Environments, SPNCE 2023

作者： Liu, Lan Wang, Pengcheng Ruan, Jianliang Lin, Jun Hu, Junhan Guangdong Polytechnic Normal University Guangzhou510655 China Sun Yat-Sen University Guangzhou510006 China China Electronic Product Reliability and Environmental Testing Research Institute Guangzhou510610 China

ISBN: (纸本)9783031736988

With the increasing complexity and volume of network traffic, accurate detection of malicious network attacks by machine learning-based network intrusion detection systems (NIDSs) remains a challenging task due to imbalanced network traffic. Conventional machine learning algorithms prioritize high overall accuracy without considering class imbalances. To address this issue, we propose ConFlow, a contrastive learning method for network intrusion detection. ConFlow leverages the Dropout layer to obtain two different vector representations of the same traffic, applying supervised contrast loss and cross-entropy loss during training. Experimental results on the ISCX-IDS2012 and CSE-CIC-IDS2017 datasets show that ConFlow outperforms other methods, especially in few-shot learning scenarios, and exhibits high generalization and robustness in real network environments. Our proposed method has significant practical implications for building an intrusion detection system with high accuracy and low false positive rates. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Network intrusion

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Motion Control Utilizing Surrogate Model for A soft Actuator Driven by Airbag-typed Cells

Motion Control Utilizing Surrogate Model for A Soft Actuator...

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2023 IEEE international conference on Robotics and Biomimetics, ROBIO 2023

作者： Yang, Yuchuan Su, Manjia Guan, Yisheng Chen, Wangcheng School of Electromechanical Engineering Guangzhou China

ISBN: (纸本)9798350325706

Most control methods for soft robots are developed based on kinematic models derived from deformation and force analysis. However, due to the non-linearity and uncertainty of soft robotic structure, it is difficult to establish accurate models, leaving a great gap in the precise control of soft robots. Recent research has shown that machine learning provides a highly effective solution. In this work, we propose a back-propagation neural network based on particle swarm optimization algorithm to establish the surrogate kinematics of a airbag-type soft actuator. Using the motion data of the soft actuator to train the network model, the corresponding relationship between the soft actuator and the end position can be obtained. The results show that the surrogate model has a good prediction effect, and the average relative error of the model is 6.4%, enabling control the motion of the soft actuator accurately enough. © 2023 IEEE.

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1st international conference on machine Intelligence for Research and Innovations, MAiTRI 2023

1st International Conference on Machine Intelligence for Res...

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1st international conference on machine Intelligence for Research and Innovations, MAiTRI 2023

ISBN: (纸本)9789819981281

The proceedings contain 29 papers. The special focus in this conference is on machine Intelligence for Research and Innovations. The topics include: Artificial Intelligence Revolution in Healthcare: From Patient Care to Disease Diagnosis;bayesian Models for Weather Prediction: Using Remote Sensing Data to Improve Forecast Accuracy;unsupervised Clustering of Asphalt Pavement Conditions Using Fuzzy C-Means Algorithm with Principal Component Analysis Aided Dimensionality Reduction;prediction of Heart Disease Risk in Early Ages with Boosting Techniques;ioT-Enabled Advanced Foam Firefighting E-Vehicle;a Study on Kerberos and Graphical Password-Based Web Authentication Scheme;application of Dragonfly Algorithm-Based Interval Type-2 Fuzzy Logic Closed-Loop Control System to Regulate the Mean Arterial Blood Pressure;challenges and Solutions with Lightweight Models for Diabetic Retinopathy Detection;Energy Management System for EV with Multiple Energy Sources;design and Development of a Charcoal-Based Sensor for Enhanced Soil Analysis in Precision Agriculture;Approach on machine learning Techniques for Anomaly-Based Web Intrusion Detection Systems: Using CICIDS2017 Dataset;enhancement of Edge Security Using Dynamic Load-Balancing Algorithm for 5G Cloud computing Network;testing and Development of a Multi-Object Tracker Based on Deep learning Techniques;artificial Intelligence Technologies-Assisted 6G;state-of-the-Art and Development of 6G Communications;machine learning Approach for Thermal Characteristics and Improvement of Heat Transfer of Nanofluids—A Review;detection of Heart Failure by Using machine learning;real-Time Road Lane Detection for Self-driving Cars Using Computer Vision;Global Horizontal Irradiance Estimation Using Bi-LSTM Algorithm;Enhancing Context-Aware Hybrid Collaborative Filtering Using DBSCAN Clustering Approach;comparative Study of Hybrid/Enhanced Nature-Inspired Optimization Algorithms for Solar Photovoltaic Model;preface.

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Federated learning for Beginners: Types, Simulation Environments, and Open Challenges

Federated Learning for Beginners: Types, Simulation Environm...

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2023 international conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023

作者： Panigrahi, Monalisa Bharti, Sourabh Sharma, Arun Indira Gandhi Delhi Technical University For Women Delhi India Munster Technological University Cork Ireland

ISBN: (纸本)9798350338003

Federated learning (FL) is a 1 decentralized machine learning (DML) technique. It was introduced by Google research team in 2017, where several clients can train a model collaboratively without sharing their raw data. Unlike centralized machine learning techniques, FL is able to preserve privacy. Since its inception, FL has gained widespread attention and has been the focus of extensive research, resulting in the development of various novel protocols, frameworks, and techniques to address its challenges. Despite these advancements, several research challenges in FL remain unexplored. While there are numerous research articles and tutorials available on FL, a beginner's guide to this domain is lacking in the literature. Therefore, this paper targets to motivate novice researchers by presenting a simplified and systematic introduction to FL. In addition to introducing FL fundamentals, this paper also highlights some open research areas in the field. © 2023 IEEE.

关键词： Edge computing

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Unmasking Audio Deception: Performance Analysis in machine learning Models with Mel-Frequency and Gammatone Frequency Cepstral Coefficients 15

Unmasking Audio Deception: Performance Analysis in Machine L...

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15th international conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Sudharsana, P.P. Rajalaxmi, R.R. Gughan, R. Thamilselvan, R. Mohana Saranya, S. Sruthi, K. Kongu Engineering College Department of Cse Perundurai India Kongu Engineering College Department of It Perundurai India

ISBN: (纸本)9798350370249

This study delves into the pervasive issue of audio deepfakes and their societal implications, emphasizing a machine learning (ML) approach for detection. Employing the Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC), the research utilizes the FOR (FAKE or REAL) Dataset to evaluate the efficacy of various models, including XGBoost, Ada Boost, SVM, LightGBM, Logistic Regression, Extra Tree, Gaussian Naïve Bayes, Decision Tree, MLP Classifier, Linear Discriminant Classifier, and Quadratic Discriminant Classifier. The study critically assesses the potential of MFCC and GFCC features in discerning synthetic audio data and employs accuracy, precision, and recall as evaluation metrics. The results highlight nuanced distinctions in the performance of each model, shedding light on their respective capabilities in distinguishing genuine from deepfake audio content. This research contributes valuable insights into the ongoing efforts to fortify societal resilience against the escalating threat of audio deepfakes, offering a comprehensive evaluation of ML models and emphasizing the significance of MFCC and GFCC features in addressing this burgeoning concern [1]. © 2024 IEEE.

关键词： Logistic regression

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1st international conference on machine Intelligence for Research and Innovations, MAiTRI 2023

1st International Conference on Machine Intelligence for Res...

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1st international conference on machine Intelligence for Research and Innovations, MAiTRI 2023

ISBN: (纸本)9789819981342

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Assessing the Quality of Wikipedia Articles 21

Assessing the Quality of Wikipedia Articles

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5th international conference on machine learning and soft computing, icmlsc 2021

作者： Dang, Quang-Vinh Industrial University of Ho Chi Minh City Viet Nam

ISBN: (纸本)9781450387613

Wikipedia is a very important information reference source for the Internet users. Due to the fact that the content of Wikipedia is the collaborative result from a massive number of participants all over the world, the quality of Wikipedia might be questionable. Over the last decade, many research works are dedicated to solve the issue of Wikipedia quality. In this paper, we present our latest research in determining the quality of Wikipedia articles. The evaluation on the real-world dataset shows that our method outperforms other baseline methods proposed recently. © 2021 ACM.

关键词： machine learning

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An Extensive Examination of Methods for Detecting and Classifying Malignant and Benign Skin Cancers Using Deep learning Techniques 3

An Extensive Examination of Methods for Detecting and Classi...

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3rd IEEE international conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Pasumarthy, Rambabu Gondi, Lakshmeeswari Gitam School Of Technology Gitam Deemed To Be University Department Of Computer Science And Engineering Andhra Pradesh Vishakhapatnam India

ISBN: (纸本)9798350318609

Skin cancer is a pressing health concern responsible for numerous fatalities in today's world, often diagnosed in its advanced stages, making treatment planning and patient survival challenging. Medical image interpretation, such as MRI as well as CT scans, is crucial for skin cancer diagnosis, but the manual analysis is costly, time-consuming, and can be influenced by biases. DL (Deep learning), a cutting-edge technology, is gaining attention as it allows for the automatic evaluation and interpretation of medical images, reducing the need for human intervention. Deep learning approaches have demonstrated remarkable outcomes in various domains by automatically extracting intrinsic image features, excluding the need for manual feature extraction. This review delves into the application of DL approaches in the diagnosis & detection of cancer, with a particular focus on skin cancer. Detecting melanoma from samples of dermoscopic skin is a formidable task, but employing a machine vision tool, like a deep learning approach, can address some of these challenges. The study introduces an automated melanoma classifier on the basis of DCNN (Deep Convolutional Neural Network) to accurately differentiate malignant from benign melanoma. It also outlines the various deep learning models and steps involved in utilizing these models for skin cancer detection. Recent advancements in the skin cancer detection Deep learning (DL) methods are explored and summarized, shedding light on the critical challenges that have been faced in accurately detecting the cancer of skin in its early stages. For this research, dermoscopic images from the international Skin Imaging Collaboration datastores (ISIC 2016, ISIC 2017, and ISIC 2020), containing diverse cancer samples, were obtained and evaluated based on metrics like precision, accuracy, specificity, recall, and F1-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Correction to: Automated facial expression recognition using exemplar hybrid deep feature generation technique (soft computing, (2023), 27, 13, (8721-8737), 10.1007/s00500-023-08230-9)

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soft computing 2024年 1-2页

作者： Baygin, Mehmet Tuncer, Ilknur Dogan, Sengul Barua, Prabal Datta Tuncer, Turker Cheong, Kang Hao Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Ardahan University Ardahan Turkey Interior Ministry Elazig Turkey Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey University of Southern Queensland ToowoombaQLD4350 Australia Faculty of Engineering and Information Technology University of Technology Sydney SydneyNSW2007 Australia Science Mathematics and Technology Cluster Singapore University of Technology and Design Singapore487372 Singapore School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

The author found that the below references were not included in the published article. Kanade T, Cohn JF, Tian Y (2000) Comprehensive database for facial expression analysis. In: Proc-4th IEEE int conf autom face gesture recognition, FG 2000, pp 46–53. Lucey P, Cohn JF, Kanade T et al (2010) The extended Cohn–Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: 2010 IEEE comput soc conf comput vis pattern recognit-work CVPRW 2010, pp 94–101. Pantic M, Valstar M, Rademaker R, Maat L (2005) Web-based database for facial expression analysis. In: IEEE int conf multimed expo, ICME 2005, pp 317–321. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2852–2861 Yin L, Wei X, Sun Y et al (2006) A 3D facial expression database for facial behavior research. In: FGR 2006 proc 7th int conf autom face gesture recognit 2006, pp 211–216. Goodfellow IJ, Erhan D, Luc Carrier P et al (2015) Challenges in representation learning: a report on three machine learning contests. Neural Netw 64:59–63. Zhang Z, Luo P, Loy CC, Tang X (2018) From facial expression recognition to interpersonal relation prediction. Int J Comput Vis 126:550–569. Chen L-F, Yen Y-S (2007) Taiwanese facial expression image database. Brain Mapp Lab Inst Brain Sci Natl Yang-Ming Univ Taipei, Taiwan Lyons MJ, Kamachi M, Gyoba J (2020) Coding facial expressions with Gabor wavelets (IVC special issue). Lyons MJ (2021) "Excavating AI" re-excavated: debunking a fallacious account of the JAFFE Dataset. Lundqvist D, Flykt A, Ohman A (1998) The Karolinska directed emotional faces (KDEF). CD ROM from Dep Clin Neurosci Psychol Sect Karolinska Institutet 2–2 Zhao G, Huang X, Taini M et al (2011) Facial expression recognition from near-infrared videos. Image Vis Comput 29:607–619. Goldberger J, Hinton GE, Roweis S, Salakhutdinov R

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An Efficient Real-Time NIDS Using machine learning Methods 4th

An Efficient Real-Time NIDS Using Machine Learning Methods

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4th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2023

作者： Goud, Konda Srikar Shivani, M. Reddy, B. V. S. Selvi Shravyasree, Ch. Reddy, J. Shreeya Department of Information Technology BVRIT Hyderabad College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9783031488870

Recent developments in network technology and related services have caused a significant rise in data traffic. However, there has also been a massive rise in the negative consequences of cyber-attacks. Many new types of network attacks are emerging. As a result, designing a robust Intrusion detection system (IDS) has become essential. This paper presents a framework for designing an efficient IDS to enhance detection accuracy and reduce false positives on real-time data. This research used the CIC-IDS 2017 dataset to train machine learning models such as Logistic Regression, K Nearest Neighbor, Gaussian Naive Bayes, and Random Forest. machine learning models often perform well on benchmark datasets but may encounter challenges when applied to real-time traffic scenarios. So, we created a Real-time dataset and tested it on the trained models. In the evaluation, the Random Forest classifier outperformed all other models and achieved an accuracy of 99.99%. © 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Botnet

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