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Energy consumption of on-device machine learning models for IoT intrusion detection

Internet of Things (Netherlands) 2023年 21卷

作者： Tekin, Nazli Acar, Abbas Aris, Ahmet Uluagac, A. Selcuk Gungor, Vehbi Cagri Cyber-Physical Systems Security Lab Department of Electrical and Computer Engineering Florida International University Miami 33174 FL United States Department of Software Engineering Erciyes University Kayseri 38280 Turkey Department of Computer Engineering Abdullah Gul University Kayseri 38080 Turkey

Recently, Smart Home systems (SHSs) have gained enormous popularity with the rapid development of the Internet of Things (IoT) technologies. Besides offering many tangible benefits, SHSs are vulnerable to attacks that lead to security and privacy concerns for SHS users. Machine learning (ML)-based Intrusion Detection systems (IDS) are proposed to address such concerns. Conventionally, ML models are trained and tested on computationally powerful platforms such as cloud services. Nevertheless, the data shared with the cloud is vulnerable to privacy attacks and causes latency, which decreases the performance of real-time applications like intrusion detection systems. Therefore, on-device ML models, in which the user data is kept locally, have emerged as promising solutions to ensure the security and privacy of the data for real-time applications. However, performing ML tasks requires high energy consumption. To the best of our knowledge, no study has been conducted to analyze the energy consumption of ML-based IDS. Therefore, in this paper, we perform a comparative analysis of on-device ML algorithms in terms of energy consumption for IoT intrusion detection applications. For a thorough analysis, we study the training and inference phases separately. For training, we compare the cloud computing-based ML, edge computing-based ML, and IoT device-based ML approaches. For the inference, we evaluate the TinyML approach to run the ML algorithms on tiny IoT devices such as Micro Controller Units (MCUs). Comparative performance evaluations show that deploying the Decision Tree (DT) algorithm on-device gives better results in terms of training time, inference time, and power consumption. © 2022 Elsevier B.V.

关键词： Energy consumption Intrusion detection IoT On-device machine learning Smart home

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Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media 16th

Impact of COVID-19 on Predicting 2020 US Presidential Electi...

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16th International Conference on Information Technology and Applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Quantifying Information Exposure by Web Browsers

Quantifying Information Exposure by Web Browsers

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Future Technologies Conference, FTC 2020

作者： Mohsen, Fadi Shehab, Mohamed Lange, Maxamilliano Karastoyanova, Dimka Department of Computer Science Bernoulli Institute University of Groningen Groningen Netherlands Department of Software and Information Systems University of North Carloina at Charlotte Charlotte United States Department of Computer Science Engineering and Physics University of Michigan-Flint Flint United States

ISBN: (纸本)9783030630911

Internet surfing entails exchanging numerous HTTP requests between clients and servers. Attached with each request is a string containing plenty of information about the client called User-Agent string. There have been numerous researches that showed the privacy and security risks of User-Agent strings. In this work we aim to minimize those risks by directing users towards using less exposing browsers. In doing so, we propose and test a new method for calculating an exposure score for web browsers based mainly on their User-Agent strings besides other attributes. We then apply this method on a dataset of over a million browsers to infer and compare their exposure scores. Finally, we present a demo web tool that shows how the resulting scores can be leveraged to aid end users. Our results reveal a variation in the exposure scores of these browsers, which creates prospect for safe Internet surfing. © 2021, Springer Nature Switzerland AG.

关键词： Web browsers

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RETRACTED ARTICLE: Industrial-IoT-hardware security-improvement using plan load optimization method in cloud

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International Journal of System Assurance engineering and Management 2023年第1-Sup期14卷 578-578页

作者： Basheer, Shakila Gopu, Magesh Mathew, Rincy Merlin Bivi, Maryam Aysha Prabu, M. Department of Information System College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Computer Science Department College of Science and Arts King Khalid University Khamis Mushayt Abha Saudi Arabia Department of Computer Science College of Computer Science King Khalid University Abha Saudi Arabia Department of Computer Science Christ University Karnataka Bangalore India

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Removing Image Artifacts From Scratched Lens Protectors

Removing Image Artifacts From Scratched Lens Protectors

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Yufei Wang Renjie Wan Wenhan Yang Bihan Wen Lap-Pui Chau Alex C. Kot School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Hong Kong Baptist University Hong Kong Distributed High Performance Software Fundamental Research Laboratory Pengcheng National Lab China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

A protector is placed in front of the camera lens for mobile devices to avoid damage, while the protector itself can be easily scratched accidentally, especially for plastic ones. The artifacts appear in a wide variety of patterns, making it difficult to see through them clearly. Removing image artifacts from the scratched lens protector is inherently challenging due to the occasional flare artifacts and the co-occurring interference within mixed artifacts. Though different methods have been proposed for some specific distortions, they seldom consider such inherent challenges. In our work, we consider the inherent challenges in a unified framework with two cooperative modules, which facilitate the performance boost of each other. We also collect a new dataset from the real world to facilitate training and evaluation purposes. The experimental results demonstrate that our method outperforms the baselines qualitatively and quantitatively. The code and datasets will be released at https://***/wyf0912/flare-removal

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CovDNet: A Hybrid Multi - Level Densely Connected Neural Network for Improvised Analysis and Classification of COVID-19 Using Chest CT-Scans

CovDNet: A Hybrid Multi - Level Densely Connected Neural Net...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Deepa S Praveenkumar G D R Suguna Vivek Duraivelu C Jamunadevi S G Sundhar Department of Computer Technology-PG Kongu Engineering College Perundurai Erode Tamilnadu Department of computer science Vet institute of Arts and Science (Co-Education) College Erode Tamilnadu Department of Computer science and Engineering Bannari Amman Institute of Technology Sathyamangalam Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana Department of Computer Technology PG Kongu Engineering College Perundurai Erode Tamilnadu Final M.Sc(Software Systems) Kongu Engineering College Perundurai Erode Tamilnadu

COVID-19, caused by the new coronavirus SARS-Co V-2, has turned into a worldwide health emergency, needing speedy and precise diagnostic techniques. This abstract provides a thorough evaluation of research works focusing on COVID-19 identification using DenseNet, a cutting-edge convolutional neural network architecture. DenseNet is notable for its innovative design, which fosters feature reuse while relieving the vanishing gradient problem and enhancing network information flow. Researchers have created novel ways for COVID-19 identification using medical imaging, such as chest X-rays and CT scans, by harnessing the capabilities of DenseNet. The analyzed studies demonstrate DenseNet's efficiency in recognizing COVID-19-specific patterns and distinguishing them from other lung diseases. These investigations have shown remarkable accuracy, sensitivity, and precision, exceeding established machine learning approaches and even professional radiologists. This research study also highlights the difficulties and constraints experienced while using DenseNet to identify COVID-19. Issues such as dataset quantity, class imbalance, and model decision-making process interpretability are addressed. Furthermore, future research paths and prospective enhancements, such as multi-modal data integration and the development of explainable AI systems, are investigated. The experimental results indicate that the proposed approach achieves high accuracy, sensitivity, and precision in the identification of COVID - 19. The proposed methodology achieved an accuracy of 89.74%, sensitivity of 87.25 % , and precision of 79.56%, which outperforms the existing state-of-the-art methods. The proposed approach is robust and can effectively differentiate between COVID-19 positive and negative cases, which is essential for early detection and prompt treatment.

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AI-Infused Telemedicine for Rural Wellness: A Comprehensive Approach

AI-Infused Telemedicine for Rural Wellness: A Comprehensive ...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Nidhi B Kurpad S Dhanyashree V Shivank T A S Surya Shobith S Vinila Jinny Computer Science and Engineering Vellore Institute of Technology Vellore India Electronics and Communication Engineering S J B Institute of Technology Bengaluru India Electrical and Electronics Engineering B N M Institute of Technology Bengaluru India Department of Software Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

The paper introduces a transformative Telemedicine Kiosk designed to enhance healthcare in remote areas by harnessing the synergy of computational intelligence and telemedicine. This advanced kiosk utilizes real-time video conferencing and comprehensive patient monitoring systems, all underpinned by Responsible AI practices that ensure ethical data management and the protection of patient privacy. Equipped with an ESP32 Wi-Fi module, the kiosk provides seamless communication between patients and medical officers, facilitating immediate care and consultation. The system goes beyond diagnostics: it supports healthcare professionals with an integrated medication dispensing mechanism powered by IoT technology. Our investigation delves into the implications of human factors, economics, and technological advances for the future of telemedicine. We present an innovative solution that not only advances healthcare accessibility but also embodies a commitment to responsible and ethical healthcare service provision in underserved regions.

关键词： Ethics Data privacy Patient monitoring Telemedicine Medical services Real-time systems Protection

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Removing Image Artifacts From Scratched Lens Protectors

arXiv

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arXiv 2023年

作者： Wang, Yufei Wan, Renjie Yang, Wenhan Wen, Bihan Chau, Lap-Pui Kot, Alex C. School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science Hong Kong Baptist University Hong Kong Distributed High Performance Software Fundamental Research Laboratory Pengcheng National Lab China Department of Electronic and Information Engineering Hong Kong Polytechnic University Hong Kong

关键词： Machine learning

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distributed AI in Zero-touch Provisioning for Edge Networks: Challenges and Research Directions

arXiv

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arXiv 2023年

作者： Hazra, Abhishek Morichetta, Andrea Murturi, Ilir Lovén, Lauri Dehury, Chinmaya Kumar Pujol, Víctor Casamayor Donta, Praveen Kumar Dustdar, Schahram Department of Computer Science and Engineering the Indian Institute of Information Technology Sri City India Communications & Networks Lab Department of Electrical and Computer Engineering National University of Singapore Singapore119260 Singapore Distributed Systems Group TU Wien Vienna1040 Austria Center for Ubiquitous Computing University of Oulu 90014 Finland Institute of Computer Science University of Tartu Tartu51009 Estonia

Zero-touch network is anticipated to inaugurate the generation of intelligent and highly flexible resource provisioning strategies where multiple service providers collaboratively offer computation and storage resources. This transformation presents substantial challenges to network administration and service providers regarding sustainability and scalability. This article combines distributed Artificial Intelligence (DAI) with Zero-touch Provisioning (ZTP) for edge networks. This combination helps to manage network devices seamlessly and intelligently by minimizing human intervention. In addition, several advantages are also highlighted that come with incorporating distributed AI into ZTP in the context of edge networks. Further, we draw potential research directions to foster novel studies in this field and overcome the current limitations. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Smart Diagnostic System For Early Detection And Prediction Of Polycystic Ovary Syndrome

Smart Diagnostic System For Early Detection And Prediction O...

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International Conference on computer Communication and Informatics (ICCCI)

作者： K P Harish M.N Dhivyanchali K.Nirmala Devi N. Krishnamoorthy R Dhana Sree R Dharanidharan Department of Computer Science & engineering Kongu Engineering College (of Affiliation) Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tiruchengode Tamilnadu INDIA Department of Software Systems and Engineering School of Information Technology and Engineering Vellore Institute of Technology Vellore Campus Tamilnadu

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset.

关键词： Support vector machines Training Stacking Null value Boosting Pregnancy test Regression tree analysis

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