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2021 IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2021

作者： Chen, Yen-Yu Liu, I-Hsien Wu, Chi-Che Liu, Chuan-Gang Li, Jung-Shian Institute of Computer and Communication Engineering National Cheng Kung University Department of Electrical Engineering Tainan Taiwan Chia Nan University of Pharmacy Science Department of Applied Multimedia and Game Development Tainan Taiwan

ISBN: (纸本)9781665437554

Recent studies have shown the practical architecture of MT6D and its performance. We obtain the highest security of MT6D with the use of the shortest changing time interval. However, it usually needs a lot of system resources, which makes the researchers consider other ways to achieve security goals in MT6D. In this paper, we propose a dynamical interval strategy for MT6D in IoT systems. According to the attacker behaviors detected by the Intrusion-detection system (IDS), we dynamically adjust the MTD system's changing time interval. The implementation method is based on the concept of finite state machines. The finite state machine of the strategy includes three states, normal state, low-speed scanned state, and high-speed scanned state, respectively. In addition, we compare the performance difference after applying our proposed strategy and find the proposed strategy can effectively reduce the system's resource usage. © 2021 IEEE.

关键词： Intrusion detection

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AI-based big data analytics model for medical applications

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Measurement: Sensors 2022年 24卷

作者： Lokesh, S. Chakraborty, Sudeshna Pulugu, Revathy Mittal, Sonam Pulugu, Dileep Muruganantham, R. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Coimbatore641062 India School of Computing Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Engineering Narsimha Reddy Engineering College Kompally Telangana State Hyderabad500100 India Department of Information Technology B K Birla Institute of Engineering and Technology Rajasthan Pilani333031 India Department of Computer Science and Engineering Malla Reddy College of Engineering and Technology Kompally Telangana State Hyderabad500100 India Department of Information Technology TKR College of Engineering and Technology Meerpet Telangana Hyderabad500097 India

Today, people's daily lives depended on their mobile phones, facilitating many beneficial tasks. Mobile computing and cloud computing combine cloud-based mobile computing to improve their features and overcoming their disadvantages such as memory space, processor, and longer battery life. By combining the four "V's" of volume, variety, velocity and veracity, Big Data analytics tools allow the utility to be extracted through knowledge. This paper explores the development of interconnected healthcare systems and the contribution of mobile cloud applications and big data analysis. With the implementation of cloud computing in the healthcare system, inspiration and development of interconnected health - software and care services are given. Big Data services for the healthcare sector on a cloudlet-based mobile cloud computing platform. Big data analysis methods, equipment, and solutions are examined. We have concluded the development of interconnected medical systems using Big Data and mobile cloud computing. © 2022 The Authors

关键词： Mobile computing

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

arXiv

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arXiv 2025年

作者： Quijano, Andrew Halkidis, Spyros T. Gallagher, Kevin Akkaya, Kemal Samaras, Nikolaos Dept. of Computer Science and Engineering New York University New YorkNY United States Dept. of Applied Informatics University of Macedonia Thessaloniki Greece NOVA LINCS NOVA School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University MiamiFL United States

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks. Copyright © 2025, The Authors. All rights reserved.

关键词： Side channel attack

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Classifying Psychiatric Patients Using Machine Learning

Classifying Psychiatric Patients Using Machine Learning

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Information Technology (ACIT)

作者： Hmmam M. Alrjoob Raed Alazaidah Radwan Batyha Hayel Khafajeh Esraa Abu Elsoud Ala'a Saeb Al-Sherideh Ali Elrashidi Muhyeeddin Alqaraleh Waleed Amer Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyper Security Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Software Engineering Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Security Faculty on Information Technology Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Psychological disorders are considered chronic illnesses that affect a wide range of populations. Some studies in the United States indicate that one in every eight individuals is affected by a psychological disorder. Additionally, individuals with a psychological disorder often have an impact on the nature of the lives of those around them. It is beneficial to detect the illness in its early stages to increase the likelihood of recovery. In this research, thirteen classification algorithms are used. Each algorithm has an interesting approach. It can be used to analyze several datasets where three datasets specific to Psychiatric patients were selected. The datasets have been chosen from UCI Machine Learning Repository. The datasets were re-analyzed after only 50% of the characteristics were approved after being evaluated by the several feature selection techniques. The main objective of this research is to find the best classifier and feature selection strategy in analyzing datasets of Psychiatric patients. After conducting numerous comparisons, it became evident that, the best classifier for the Autism-Adult-Data dataset are Bagging and Random Forest. As for the best learning strategy, it was found to be Functions and Trees, the best feature selection was obtained using Symmetrical Uncourt Attribute Evaluation. Both IBK and KStar classifier proved to be the best classifier for the Parkinson's dataset, Lazy demonstrated to be the most effective learning strategy, Correlation Attribute Evaluation technique yielded the best feature selection. Logistic emerged as the best classifier for planning-relax dataset, the best learning strategy was Bayes, the best feature selection was found to be Symmetrical Uncert Feature Selection.

关键词： Machine learning algorithms Correlation Psychology Feature extraction Classification algorithms Bayes methods Information technology Random forests Bagging Logistics

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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A Generalization of Multi-Source Fusion-Based Framework to Stock Selection

SSRN

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SSRN 2023年

作者： Snášel, Václav Velasquez Silva, Juan Domingo Pant, Millie Georgiou, Dimitrios Kong, Lingping Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Czech Republic Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Uttarakhand Roorkee247667 India Departament of Industrial Engineering University of Chile Santiago Chile Faculty of Electrical Engineering and Computer Science National Technical University of Athens Greece Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Uttarakhand Roorkee247667 India

Selecting outstanding tech stocks for investment is challenging. Specifically, studying the investment for academic purposes is not mature enough due to the disarray of the publications and the `over' informative experience from profit-making websites. Often some authors entitle stock price prediction subject as a stock selection problem;both prediction and selection are just sub-section of portfolio management. Moreover, stock websites provide tons of potential criteria showing their perspective evaluation to simulate and monitor the stock market professionally, which causes an extra problem for academic studies screening out the less informative criteria. The paper generalizes a novel framework with a user-interactive interface for stock selection problems based on multi-source data fusion and decision-level fusion to enhance reliability that is limited by the narrow criteria performance and to enhance the strength of a model overcoming the weakness of a single-performed model. This framework benefits the time-series prediction and decision-making study. Besides, we propose adopting dynamic time warping to assist a task-learning process by customizing a loss function that improves the accuracy of data prediction. The experiment shows that the proposed method reduces the prediction log error by 28\% in a best case of real-situation data and decreases the warping cost by 20\%. Finally, we illustrate the proposed framework by implementing it in a real-world stock data selection. The results are practical and effective, further justified through a detailed ablation study. © 2023, The Authors. All rights reserved.

关键词： Investments

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Stability Criteria for Neural Networks with Stochastic Impulsive Intensity and Impulsive Density

Stability Criteria for Neural Networks with Stochastic Impul...

引用

Neuromorphic Computing (ICNC), International Conference on

作者： Hao Deng Jingxin Liu Jun Peng Yingxu Wang Fei Chang School of Computer Science and Engineering Chongqing University of Science and Technology Chongqing China College of Electronic and Information Engineering Southwest University Chongqing China Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning China School of Mathematics and Physics Sciences Chongqing University of Science and Technology Chongqing China Chongqing Research Institute of Intelligent Mathematics and Autonomous AI (RI-IM·AI*) Chongqing China

ISBN: (数字)9798331508661

ISBN: (纸本)9798331508678

This paper studies stability conditions for neural networks with stochastic impulsive intensity and impulsive density. Firstly, the pth moment exponential stability conditions are given, where the impulsive intensities of impulsive neural networks are independent and identically distributed, and the impulsive sequence is defined by impulsive density. Then, taking a linear time-varying impulsive system as an example, it is shown that impulsive sequence defined by average impulsive interval is sometime insufficient in terms of stabilizing such linear time-varying systems. Finally, two examples are presented to verify that impulsive density method is more effective and universal than average impulsive interval method when dealing with time-varying systems.

关键词： Sufficient conditions Stability Neuromorphic engineering Neural networks Stability criteria Control theory Time-varying systems Numerical stability

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Ultra-low energy programmable non-volatile silicon photonics based on graphene heaters

Ultra-low energy programmable non-volatile silicon photonics...

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CLEO: science and Innovations, S and I 2022

作者： Fang, Zhuoran Chen, Rui Zheng, Jiajiu Khan, Asir Intisar Neilson, Kathryn M. Saxena, Abhi Chen, Michelle Rios, Carlos Hu, Juejun Pop, Eric Majumdar, Arka Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Physics University of Washington SeattleWA98195 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States Department of Materials Science and Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics & Applied Physics University of Maryland College ParkMD20742 United States Department of Materials Science and Engineering Massachusetts Institute of Technology MA02139 United States

We demonstrate a nonvolatile electrically programmable phase-change silicon photonic switch and phase shifter leveraging a monolayer graphene heater with record-high programming energy efficiency (8.7±1.4 aJ/nm3)... 详细信息

ISBN: (纸本)9781557528209

关键词： Silicon photonics

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MicroPython-based Sensor Node with Asymmetric Encryption for Ubiquitous Sensor Networks

MicroPython-based Sensor Node with Asymmetric Encryption for...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Ulrich Ter Horst Hagen Hasberg Stephan Schulz Faculty of Engineering and Computer Science Hamburg University of Applied Sciences Hamburg Germany

This work introduces a new microcomputing node with long-term resistant data security, based on asymmetric and symmetric encryption combined with the modern and established scripting language Python. The presented microcomputing node integrates a MicroPython runtime environment to address a wide audience of application engineers as user base instead of a selected group of embedded engineers, who have deep knowledge in programming IoT devices using C/C++. It combines its scripting capabilities with security features of modern smartcards and secure cellular networking based on 4G.

关键词： Knowledge engineering Resistance Runtime environment Embedded systems Switches Programming Encryption

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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