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Proceedings of the 2023 12th International Conference on Networks, communication and Computing

作者： Reatrey Pich Sokchenda Sreng Jean-Noel Colin Department of Information and Communication Engineering Institute of Technology of Cambodia Cambodia Department of Telecommunication and Network Engineering Institute of Technology of Cambodia Cambodia Faculty of Computer Science University of Namur Belgium

ISBN: (纸本)9798400709265

DNS over HTTPS (DoH) Tunneling is a technique used to encapsulate the information or programs of other protocols into the DNS protocol with the encrypted form for communication. On the strength of DNS as the core component in internet communication, DoH straightens data privacy with less restriction and qualification of tunneling techniques, the attackers take advantage of exploring DoH Tunneling traffic to initial and deploy their activities, such as command and control communication and data exfiltration. Keeping their assets safe is becoming a critical issue for every organization. Based on our knowledge and investigation, currently, there are only flow-level feature analysis solutions that have been made, and they don't have any methods using single packet information to detect the DoH Tunneling traffic. it can't be used passive data analysis methods such as the active detection system's flow-level detection system. Therefore, in this paper, the first packet-level feature analysis solution to detect DoH Tunneling traffic by using only five selected features from the packet and a study of the number of packets inside flow learning for this traffic detection is proposed. As a result, Random Forest can perform well, with a high accuracy of up to 99.93% among other algorithms.

关键词： DoH Tunneling Flow-level Features Intrusion Detection system Machine Learning Algorithms Packet-level Features

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A Hybrid Deep Learning Neural Network for Recognizing Exercise Activity Using Inertial Sensor and Motion Capture system 4

A Hybrid Deep Learning Neural Network for Recognizing Exerci...

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4th International Conference on Big Data Analytics and Practices, IBDAP 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350300192

The recognition of human activities holds great significance in the fields of AI and human-computer interaction studies. Human activity recognition (HAR) finds extensive application, including in behavioral assessment, gesture identification, fall detection, and gait evaluation. Recent strides in wearable technology and AI have showcased the potential to accurately classify movement events for HAR, achieved by analyzing time-series data collected from wearable sensors. This study focuses on deep learning networks, particularly CNN and LSTM, to extract spatial and temporal features from wearable sensor data for efficient exercise activity classification. A comprehensive assessment compared two sensor data types: 3-axis linear acceleration from IMUs and 3D acceleration from MOCAP markers. Findings highlight comparable accuracy between IMUs and MOCAP in exercise identification. The hybrid CNN-LSTM demonstrated high efficacy, achieving remarkable F1-scores of 98.85% for IMU and 98.23% for MOCAP data. © 2023 IEEE.

关键词： Wearable sensors

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Digital Technology in Health Education Trend Mapping through Bibliometrics Analysis 4

Digital Technology in Health Education Trend Mapping through...

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4th International Conference on Informatics, Multimedia, Cyber and Information system, ICIMCIS 2022

作者： Maulana, Fairuz Iqbal Lestari, Dian Widartha, Vandha Pradwiyasma Haidar, Nirwana Dirgantara, Wahyu School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Airlangga University Faculty of Medicine Surabaya60132 Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia

ISBN: (纸本)9781665473279

This study examines the mapping of research data on digital technology in the field of health education using bibliometric analysis method. Data was collected by identifying keywords in the Scopus database and sorting them out to sort the studies from the last 10 years (2012-2021). Through this step, a total of 1482 documents were obtained (articles, journals, proceedings, books, and others). The data is then processed using the VOSViewer instrument to obtain a visualization of the mapping analysis. This study also analyzes the network types of authors and co-authors through the VOSViewer instrument. The result of this study indicates that the most document types published within ten years are Articles (66.5%), Review (22.9%), Conference Paper (4.2%), and others. The most studied subjects are Medicine (55.7%), Nursing (9.0%), Health Professions (8.2%), Social sciences (7.8%), engineering (3.9%), computer science (2.6%), Environmental science (2.6%), Biochemistry-Genetics and Molecular Biology (2.2%), Psychology (1.6%), and Dentistry (1.3%). This study offers a written communication process and the nature and direction of developing descriptive means of counting and analyzing the various phases of communication as well as recognizing the authorship and direction of its symptoms in documents on the subject of digital technology in the health sector. © 2022 IEEE.

关键词： Mapping

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Deep Learning Approaches for Epileptic Seizures Recognition based on EEG Signal 46

Deep Learning Approaches for Epileptic Seizures Recognition ...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350303964

Recognition system of human actions is a popular research field in healthcare which is essential in identifying abnormal patient activities to estimate their psychological state. Epileptic seizures are a common neurological disorder affecting millions of individuals globally. Early and accurate diagnosis of epilepsy can lead to up to 70% of seizure-free patients. There is a requirement for smart and automated HAR systems to aid clinicians in accurately diagnosing neurological disorders. This study examines the latest deep learning networks for time-series classification to detect epileptic seizures using EEG signal data. To enhance recognition performance, a hybrid residual network called Inception-ResNet is presented for the binary classification of epileptic activities. Through thorough analysis, the Inception-ResNet model was observed to provide the best results, with an average accuracy and F1-score of 99.61%. © 2023 IEEE.

关键词： Electroencephalography

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A Lightweight Deep Residual Network for Recognizing Activities in Daily Living Using Channel State Information 14

A Lightweight Deep Residual Network for Recognizing Activiti...

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14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350336269

The field of human activity recognition (HAR) focuses on predicting human motion and actions by analyzing data from various sensors. HAR tasks can benefit from vision-based and sensor-based approaches, which offer high data quality. However, it's essential to recognize that these methods have drawbacks, such as intrusiveness and privacy concerns. With the increasing prevalence of Wi-Fi devices, Wi-Fi technology has gained popularity for tracking everyday activities, particularly among senior individuals, making it an appealing choice in healthcare applications. Channel State Information (CSI), a crucial attribute of Wi-Fi signals, can be leveraged to recognize different human actions. This study introduces CSI-ResNeXt, a lightweight deep residual network designed to classify human actions using CSI data from daily living scenarios. A series of tests were conducted to evaluate the network's performance, using a standard CSI-HA dataset. The results of the experiments demonstrated that the proposed lightweight deep residual network achieved an impressive accuracy of 99.17% and an F1-score of 99.10%, surpassing the performance of previous deep learning models. © 2023 IEEE.

关键词： Classification (of information)

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Deep Learning Approaches for HAR of Daily Living Activities Using IMU Sensors in Smart Glasses 8

Deep Learning Approaches for HAR of Daily Living Activities ...

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Joint 8th International Conference on Digital Arts, Media and Technology and 6th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit School of Information and Communication Technology University of Phayao Department of Computer Engineering Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350396546

In the latest days, study into the development of intelligent technologies has proven valuable, contributing to attempts to improve the quality of human existence. Smart glass is one of the intelligent wearable devices that can be used for various purposes, including healthcare monitoring, fall detection, sleep tracking, and human activity recognition (HAR). Smart-phones and smartwatches are the primary wearables utilized in sensor-based HAR to collect human motions for training recognition models based on physical movement. These wearable tools, nevertheless, are more intrusive than smart glasses. Using IMU sensor data acquired via smart glasses, we investigate deep learning algorithms for detecting people's activities of daily living (ADL). This work proposes a hybrid deep neural network that automatically extracts spatial-temporal information from raw data to enhance identification p erformance. We performed tests to evaluate deep learning models using a publically available benchmark dataset, UCA-EHAR, which included IMU sensor data from multiple ADL from smart eyewear. The recommended CNN-LSTM model achieved the best effectiveness with the highest F1-score of 93.20%, as determined by experimental findings. © 2023 IEEE.

关键词： Deep neural networks

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GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning

arXiv

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arXiv 2024年

作者： Qu, Sanqing Zou, Tianpei Röhrbein, Florian Lu, Cewu Chen, Guang Tao, Dacheng Jiang, Changjun The School of Automotive Engineering Department of Computer Science Tongji University China The Faculty of Computer Science Chemnitz University of Technology Germany The Department of Computer Science Shanghai Jiao Tong University China The School of Computer Science Faculty of Engineering The University of Sydney Australia The Department of Computer Science Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University China

Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted to closed-set scenarios. In this paper, we explore Source-Free Universal Domain Adaptation (SF-UniDA) aiming to accurately classify "known" data belonging to common categories and segregate them from target-private "unknown" data. We propose a novel Global and Local Clustering (GLC) technique, which comprises an adaptive one-vs-all global clustering algorithm to discern between target classes, complemented by a local k-NN clustering strategy to mitigate negative transfer. Despite the effectiveness, the inherent closed-set source architecture leads to uniform treatment of "unknown" data, impeding the identification of distinct "unknown" categories. To address this, we evolve GLC to GLC++, integrating a contrastive affinity learning strategy. We examine the superiority of GLC and GLC++ across multiple benchmarks and category shift scenarios. Remarkably, in the most challenging open-partial-set scenarios, GLC and GLC++ surpass GATE by 16.7% and 18.6% in H-score on VisDA, respectively. GLC++ enhances the novel category clustering accuracy of GLC by 4.3% in open-set scenarios on Office-Home. Furthermore, the introduced contrastive learning strategy not only enhances GLC but also significantly facilitates existing methodologies. Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： Velmurugan M Shinzeer C K G. Maya R. Ramya R Kanimozhi Syed Ismail Abdul Lathif Department Of Computer Science & Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Department of School of Computer Applications Lovely Professional University Phagwara Punjab Faculty of Management SRM Institute of Science and Technology Kattankulathur Department of Electronics and Communication Engineering K.Ramakrishnan College of Engineering Samayapuram Computer Science and Engineering Muthayammal Engineering College Rasipuram Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions.

关键词： Resistance Privacy Fault tolerance Smart contracts Fault tolerant systems Throughput Energy efficiency Blockchains Identity management systems Interoperability

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Improving Inertial Sensor-based Human Activity Recognition using Ensemble Deep Learning 8

Improving Inertial Sensor-based Human Activity Recognition u...

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作者： Rojanavasu, Pornthep Jantawong, Ponnipa Jitpattanakul, Anuchit Mekruksavanich, Sakorn School of Information and Communication Technology Department of Computer Engineering University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut's University of Technology North Bangkok Department of Mathematics Bangkok Thailand

ISBN: (纸本)9798350396546

Sensor-based human activity recognition (S-HAR) is a famous study focusing on detecting human physiological actions by interpreting various sensors, especially one-dimensional time series information. Typically, S-HAR machine learning methods were developed using handcrafted characteristics. Unfortunately, this is a complicated process that involves feature engineering and a high level of domain knowledge. Due to the development of deep neural networks, classification techniques could efficiently handle relevant characteristics from raw sensor data, resulting in enhanced classification outcomes. In this study, we describe a unique method for S-HAR based on ensemble deep learning with sensor nodes connected to the waist, chest, leg, and arm. Implementing and training three deep learning networks is performed using a publically available dataset, including wearable sensors from eight human actions. The findings demonstrate that the proposed Ens-ResNeXt model provides the maximum accuracy and F1-score, which is superior to existing techniques. © 2023 IEEE.

关键词： Learning systems

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Recognizing and Understanding Sport Activities based on Wearable Sensor Signals using Deep Residual Network 4

Recognizing and Understanding Sport Activities based on Wear...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2023

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350330618

The prevalence of intelligent wearable devices has significantly increased, offering advantages to individuals of all age groups. In the field of human activity recognition (HAR) research, wearable sensor data plays a crucial role in classifying various human actions. Sensor-based assessments are commonly employed in hazard analysis and risk assessments. However, traditional machine learning (ML) algorithms have faced challenges when applied to sports activities due to their unpredictable nature. These challenges have prompted the need for manual feature extraction, limiting the efficacy of ML methodologies in data categorization. To address this, our study introduces ResNet, a deep residual network, for effectively categorizing sports activities using wearable sensor data. To evaluate our proposed approach, we utilized a publicly available benchmark dataset consisting of sensor data collected from the dominant wrist, neck, and thigh of each study participant. The experimental results demonstrated the exceptional performance of the ResNet model, achieving an impressive accuracy rate of 99.83% in recognizing sport activities. Additionally, we compared the efficacy of our suggested model with five fundamental deep learning models, namely CNN, LSTM, BiLSTM, GRU, and BiGRU. The comparative analysis revealed that the ResNet model outperformed the other deep learning models, demonstrating superior performance in sport activity recognition. ©2023 IEEE.

关键词： Wearable sensors

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