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14th IEEE Annual Ubiquitous computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Wang, Minxiao Yang, Ning Weng, Ning Southern Illinois University Computer Engineering Program School of ECBE CarbondaleIL United States Southern Illinois University Information Technology Program School of Computing CarbondaleIL United States

ISBN: (纸本)9798350304138

Deep learning (DL) has been proposed as a promising solution for network intrusion detection systems (NIDSs). While most DL-based NIDSs focus on high accuracy, they often overlook the critical issue of NIDSs response time for intrusion behavior. Recently some existing works presented time-aware evaluation metrics or design DL-based solutions for early intrusion detection, yet they primarily focus on the post-performance of NIDSs, without thoroughly investigating the impact of early intrusion detection on DL-based NIDSs due to the black-box nature of DL models. To address this research gap, we explore the impact of early detection on DL-based models' detection accuracy and input features. In our empirical study, we implement two different existing deep learning-based NIDSs methods and evaluate them on three published benchmark NIDSs datasets. Based on the experiment results on DL-based NIDSs detection accuracy, we observe that the early detection scenario will lead to the NIDSs model's detection accuracy decreasing up to 50%. To understand the reasons behind it, we adopt the SHapley Additive exPlanations (SHAP) to analyze the importance of features and we observe the varying feature distributions. The feature importance and feature distribution results show that early intrusion detection may lead to a degradation of feature quality. © 2023 IEEE.

关键词： Intrusion detection

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Integrating Data-Driven Security, Model Checking, and Self-adaptation for IoT Systems Using BIP Components: A Conceptual Proposal Model 1st

Integrating Data-Driven Security, Model Checking, and Self-a...

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1st International Conference on Advances in computing Research, ACR’23

作者： Alsobeh, Anas Shatnawi, Amani Program School of Computing Southern Illinois University CarbondaleIL United States Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan

ISBN: (纸本)9783031337420

Efficiency and convenience have increased as a result of the Internet of Things’ (IoT) expanding adoption across several enterprises. Nevertheless, the dynamic nature of IoT devices severely hinders real-time security breach prevention. Model checking, self-adaptation, and the Behavior-Interaction-Priority (BIP) component model are combined into an application layer to increase data-driven security for IoT systems. An integrated approach is proposed in this paper as a conceptual model for Internet of Things (IoT) security. This approach may have the potential to significantly improve the identification and mitigation of security concerns, including bogus data, malware attacks, and secure system transactions. The framework model is comprised of four primary components: BIP-based Secure Data Collection and Storage (BIP-SDCS), BIP-based Data Analytics (BIP-DA), BIP-based Secure Threat Detection and Response (BIP-STDR), BIP-based Continuous Monitoring (BIP-CM). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Model checking

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Comprehensive Evaluation of the Lakeside Café Point of Sale (POS) Application: A Synergistic Analysis Using SUS and EUCS Models 25

Comprehensive Evaluation of the Lakeside Café Point of Sale...

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25th International Seminar on Intelligent technology and Its Applications, ISITIA 2024

作者： Wibowo, Jonathan Arya Yasirandi, Rahmat Utomo, Rio Guntur Pratama Anom, Rahmat Indra Telkom University School of Industrial Engineering Bandung Indonesia King Mongkut's Institute of Technology Ladkrabang Faculty of Information Technology Bangkok Thailand Telkom University School of Computing Bandung Indonesia Trisakti Institute of Tourism Doctoral Program of Tourism Jakarta Indonesia

ISBN: (纸本)9798350378573

Lakeside Café, located in the Telkom University, processes approximately 80-100 drink transactions daily. Having used their current point-of-sale (POS) application for over a year, an urgent evaluation of its performance and user satisfaction is necessary to ensure ongoing efficiency. This study employs the System Usability Scale (SUS) model and the Extended Unified Theory of Acceptance and Use of technology (EUCS) model. This combined approach enhances the understanding of the application's strengths and identifies areas for improvement, forming a solid foundation for evaluating user satisfaction. The study reveals notable usability of the Lakeside Café POS application, evidenced by an SUS score of 78.8, ranking it as "ACCEPTABLE"and "GOOD."The EUCS model analysis further highlights potential enhancements, with the Content and Functionality dimensions scoring 3.5 and 3.65, respectively, indicating room for workflow process development. These findings emphasize the need for improved content delivery and strengthened application functionality to boost user satisfaction and maximize system performance. This research enriches the dialogue on technological acceptance, providing valuable insights for developers and stakeholders focused on advancing POS applications. The use of SUS and EUCS models offers a comprehensive evaluation method, fostering continuous advancements and innovations in digital solutions within the food and beverage sector. © 2024 IEEE.

关键词： Sales

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Defect Recognition on Single Layer Bare Printed Circuit Boards for Quality Control and Visual Inspection: A Low-Sample-Size Deep Transfer Learning Approach 9

Defect Recognition on Single Layer Bare Printed Circuit Boar...

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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Technological Institute of the Philippines Graduate School Program Quezon City Philippines Isabela State University College of Computing Science Information and Communication Technology Isabela Cauayan City Philippines

ISBN: (纸本)9798350369922

Defect detection is a critical component in ensuring the highest quality of printed circuit board manufacture. The work investigates the application of transfer learning to identify YOLOv8 design flaws. The YOLOv8 model performs exceptionally well, with mean Average Precision (mAP) of 98.3%, Precision of 98.6%, and Recall of 96.0%. Numerous datasets provide high detection rates across a range of fault types following thorough testing and inference. This extensive evaluation demonstrates the robustness of the model by reliably identifying Fissure, Mousebite, EC-Short, MC-Spur, Breakout, Pinhole, and Copper defects. The study emphasizes how the YOLOv8 design can identify flaws, providing a dependable and effective way to guarantee the dependability and caliber of printed circuit boards throughout the production process. The foundation for utilizing YOLOv8 in industrial quality control is established by this study, which holds significant potential for improving defect detection and quality control in the manufacturing industry. © 2023 IEEE.

关键词： Printed circuit boards

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Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks 2

Defect Detection and Classification in Printed Circuit Board...

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2nd International Conference on Edge computing and Applications, ICECAA 2023

作者： Austria, Yolanda D. Fajardo, Arnel C. Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Isabela Cauayan City Philippines

ISBN: (纸本)9798350347579

PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated methods using deep learning models have gained preference. These models learn from diverse datasets of defect images or videos. In this study, a deep learning model was developed and trained using a dataset containing various defect classes, including breakout, copper, crack, cut, excessive conductor, missing conductor, mouse bite, pinhole, spur, and scratch. The model showed promising results in detecting defects across all classes. © 2023 IEEE.

关键词： Image segmentation

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Watermelons Talk: Predicting Ripeness through Tapping

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IEEE Internet of Things Magazine 2024年第4期7卷 154-161页

作者： Lin, Yun-Wei Lin, Yi-Bing Chen, Wen-Liang Chang, Chia-Hui Li, Han-Kuan College of Artificial Intelligence Yang Ming Chiao Tung University (NYCU) NYCU China Medical University Miin Wu School of Computing National Cheng Kung University Research Center for Information Technology Innovation Academia Sinica College of Biological Science and Technology NYCU Industrial Development Graduate Program of College of Biological Science and Technology

During the commercial production of watermelons, farmers must swiftly assess fruit ripeness post-harvest to minimize losses through sorting based on edibility time. This process enhances marketability and productivity but is often very tedious in traditional approaches. This article delves into the multifaceted realm of Internet of Things (IoT) based real-time watermelon ripeness evaluation. Watermelons, subject to diverse degrees of ripeness, significantly impact the fruit's taste and texture. Notably, watermelons cease to mature after detachment from the vine, underscoring the importance of selecting the ripest specimens at purchase. Prompt post-harvest fruit ripeness assessment is pivotal to mitigate losses, ensuring accurate sorting based on edibility timeline. Consequently, diligent watermelon ripeness assessment by farmers gains importance for enhanced marketability and productivity. While manual techniques like tapping, color examination, and day counting serve practical purposes, their accuracy relies on subjective judgment. Currently, the prevailing method for assessing watermelon ripeness is the sound test. This tapping technique surprisingly rests on logical grounds, as the resulting sounds offer an adequate ripeness indicator. However, personal interpretations of these sounds are influenced by subjective experiences and traditional wisdom. This article investigates non-destructive methodologies for evaluating watermelon ripeness. Then we propose WatermelonTalk, an IoT based real-time deep learning platform designed for acoustic watermelon testing. We also introduce the concept of the 'tapping ensemble,' not previously found in the literature, which significantly enhances prediction accuracy. The article's contributions encompass the most comprehensive categorization of watermelons in the literature, specifically categorizing 1698 watermelons across 343 varieties by ripeness. Previous studies have considered either the 2-level test (unripe and ripe) or th

关键词： Productivity Deep learning Accuracy Real-time systems Acoustics Crops Farming Agricultural products Commercialization Color Testing

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Lightweight Authenticated Key Agreement Protocol for Smart Power Grid Systems Using PUF

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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OSM: Leveraging model checking for observing dynamic 1 behaviors in aspect-oriented applications

arXiv

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arXiv 2024年

作者： AlSobeh, Anas Mohammad Ramadan Department of Information Systems Faculty of Information Technology and Computer Science Yarmouk University Irbid Jordan Program of Information Technology School of Computing College of Engineering Computing Technology and Mathematics Southern Illinois University CarbondaleIL United States

In the intricate domain of software systems verification, dynamically model checking multifaceted system characteristics remains paramount, yet challenging. This research proposes the advanced observe-based statistical model-checking (OSM) framework, devised to craft executable formal models directly from foundational system code. Leveraging model checking predicates, the framework melds seamlessly with aspect-oriented programming paradigms, yielding a potent method for the analytical verification of varied behavioral attributes. Exploiting the transformative capacity of OSM framework, primary system code undergoes a systematic metamorphosis into multifaceted analysis constructs. This not only simplifies the model verification process but also orchestrates feature interactions using an innovative observing join point abstraction mechanism. Within this framework, components encompassing parsing, formal verification, computational analytics, and rigorous validation are intrinsically interwoven. Marrying the principles of model checking with aspect-oriented (AO) modularization, OSM framework stands as a paragon, proficiently scrutinizing and affirming system specifications. This ensures the unyielding performance of electronic health record systems amidst shifting preconditions. OSM framework offers runtime verification of both object-oriented and AO deployments, positioning itself as an indispensable open-source resource, poised to automate the enhancement of system performance and scalability. Copyright © 2024, The Authors. All rights reserved.

关键词： Aspect oriented programming

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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Patient Engagement through Mobile Health Interventions of Diabetes: A Systematic Literature Review 33

Patient Engagement through Mobile Health Interventions of Di...

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33rd Australasian Conference on information Systems: The Changing Face of IS, ACIS 2022

作者： Alhubayshi, Arwa Almutairi, Nawaf Win, Khin Than Information Technology Department Medical Cities Program – MOI Riyadh Saudi Arabia School of Computing and Information Technology University of Wollongong Wollongong Australia Faculty of Public Health and Health Informatics University of Hail Hail Saudi Arabia

This paper aims to review the applicability of the Healthcare information and Management Systems Society (HIMSS) patient engagement framework and to recognise the patient engagement features presented in current mHealth interventions for improving engagement in diabetes self-management. Scopus, Web of Science and PubMed databases were searched for relevant studies. The selected studies were then analysed using the Healthcare information and Management Systems Society (HIMSS) patient engagement framework. Twenty engagement features were identified from 29 studies reviewed. Half of the HIMSS patient engagement framework categories were seen in the studies. This review shows that mHealth interventions have achieved the third and fourth phases of the HIMSS patient engagement framework. In addition, this review suggests improvements in patient engagement facilitation, including integrating patient-generated data into electronic health records, collaboration access to the health records, and peer support through online community forums. Finally, the future patient engagement framework is discussed for mHealth technology. Copyright © 2022 Arwa Alhubayshi, Nawaf Almutairi, Khin Than Win.

关键词： mHealth

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