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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Veeraiah, Vivek Pankajam, A. Vashishtha, Ela Dhabliya, Dharmesh Karthikeyan, P. Chandan, Radha Raman Adichunchanagiri University Department of R & D Computer Science Karnataka Mandya India Avinashilingam Institute for Home Science and Higher Education for Women Dep of Business Administration Tamil Nadu India University of North Texas Health Science Center Healthcare Consultant TX United States Vishwakarma Institute of Information Technology Dept. of Information Technology Maharashtra Pune India School of Management Studies KonguEngineering College Perundurai Tamil Nadu Erode India Shambhunath Institute of Engineering & Technology Jhalwa Dept. of Computer Science & Engineering Uttar Pradesh Prayagraj India

ISBN: (纸本)9798350398267

Internet of Things (IoT) has made it possible to diagnose and treat patients remotely, as well as to expedite the transportation of essential drugs and medical equipment to locations that are geographically separated. This has occurred at a time when society has become more socially distant. During the Ebola and COVID-19 outbreaks, the Internet of Things (IoT) technology was put to use in remote patient monitoring and the management of the vaccine cold chain. Concurrently, this study reflects on the variables that are required for IoT to scale. Since December 2019, the COVID-19 outbreak on a worldwide scale has developed into a significant problem. In order for medical treatment to be successful, it is essential to make a prompt and accurate diagnosis of persons who may be infected with the COVID-19 virus. In order to put a halt to the spread of COVID-19, it is important to construct an automated system that is based on deep transfer learning and is capable of detecting the virus based on chest X-rays. The authors of this study present an internet-of-things (IoT) system that makes use of ensemble deep transfer learning to diagnose COVID-19 patients at an earlier stage. It is feasible to keep an eye on potentially hazardous COVID-19 incidents as they occur so long as suitable procedures are adhered to. Inceptions A variety of different deep learning models are included into the framework that has been proposed for the Internet of Things. According to the findings of the study, the method that was suggested assisted radiologists in accurately and quickly identifying patients who could have COVID-19. The proposed effort focuses on developing an effective identification system based on the COVID-19 standard for use in an IoT setting. © 2022 IEEE.

关键词： COVID-19

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Security Threats Mitigation for Internet of Vehicles Using Cryptographic Protocols

Security Threats Mitigation for Internet of Vehicles Using C...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Khurram Shahzad Muhammad Raheel Raza Asaf Varol Dept. of Electronic & Comp. Engineering University of Limerick Limerick Ireland Peter Faber Business School Australian Catholic University North Sydney Australia Dept. of Eng. Management & Technology College of Eng. & Comp. Science Uni. of Tennessee at Chattanooga Chattanooga TN US Department of Computer Engineering College of Eng. & Natural Sciences Maltepe University Istanbul Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

In the transportation domain, the Internet of Vehicles (IoV) enables vehicles to connect with their surroundings, including other vehicles, infrastructure, and various services, hence promoting information sharing for better user experience, safety, and traffic management. Given the increasing demand for autonomous vehicles, it is critical to address the network's security and privacy vulnerabilities. The paper aims to propose a secure chat protocol specifically tailored for enhancing communication in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) relationships within the Internet of Vehicles (IoV). The protocol establishes a mutually agreed session key (Kabc) among vehicles and infrastructure components, ensuring that all exchanged messages are encrypted to protect sensitive data from eavesdropping and unauthorized access. By employing a certificate-based authentication mechanism, the protocol verifies the identities of communicating entities, thereby preventing impersonation attacks and ensuring trust in the information exchanged between vehicles and infrastructure systems.

关键词： Vehicle-to-infrastructure Prevention and mitigation Transportation Vehicular ad hoc networks Information sharing Network security User experience Safety Cryptography Internet of Vehicles

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Evolutionary Algorithms for Deep Learning in Secure Network Environments

Evolutionary Algorithms for Deep Learning in Secure Network ...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Banothu Seva Ratnesh Jain Neha Khare Hussain Ahmed Choudhury M Ayyappa Chakravarthi Nitin Tomer Dept. of CSE MLR Institute of Technology Hyderabad Telangana India Dept of Computer Science & Design GGITS Jabalpur M.P India Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P India Department of CSE Barak Valley Engineering College Karimganj Assam India Dept. of CSE – Data Science KKR & KSR Institute of Technology and Sciences Guntur AP India Dept of IOT Cyber Security & Blockchain GGITS Jabalpur M.P India

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Hackers are getting better at breaking into protected networks, making network security an arms race. Today, combining EAs with DL to combat these risks is cuttingedge. This research illustrates how EAs and DL may enhance network security in a shifting threat environment. This blend outperforms traditional techniques in many critical security measures, according to our study. Network safety in deep learning involves improving model architecture, hyperparameters, and adaptive strategies. These variables affect how successfully a model detects unexpected activity while limiting false positives. We propose rethinking how these settings are fine-tuned and modified to create a larger digital asset security strategy. This technique integrates EAs with DL. Evolutionary algorithms, based on natural selection and genetic variety, solve large problem areas well. With DL, these approaches speed up the construction of cutting-edge safety tools. These approaches allow us to find model designs, modify hyperparameters, and update trained models, which we like. Our experiment illuminated the topic. The recommended method routinely outperforms established methods in terms of discovery rate. In network security, the detection rate indicates how successfully a system can identify and react to malicious conduct. Our approach can protect digital products due to their greater detection rate. Security specialists celebrate any modest finding in their never-ending battle against nefarious actors. Network security professionals must monitor false positives to avoid too many false alarms. New dangers may be stopped before they do harm. Evolutionary algorithms and deep learning in secure networks give new perspectives. Empirical evidence reveals that our method is more efficient, versatile, and finds things better. Because digital threats change, our strategy strengthens our defenses to protect critical networks and digital assets.

关键词： Deep learning Adaptation models Limiting Computational modeling Weapons Evolutionary computation Network security Personnel Protection Monitoring

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Inferring Topology of Networked Dynamical Systems by Active Excitations

Inferring Topology of Networked Dynamical Systems by Active ...

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IEEE Conference on Decision and control

作者： Yushan Li Jianping He Cailian Chen Xinping Guan Dept. of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Topology inference for networked dynamical systems (NDSs) has received considerable attention in recent years. The majority of pioneering works have dealt with inferring the topology from abundant observations of NDSs, so as to approximate the real one asymptotically. Leveraging the characteristic that NDSs will react to various disturbances and the disturbance’s influence will consistently spread, this paper focuses on inferring the topology by a few active excitations. The key challenge is to distinguish different influences of system noises and excitations from the exhibited state deviations, where the influences will decay with time and the excitation cannot be arbitrarily large. To practice, we propose a one-shot excitation based inference method to infer h-hop neighbors of a node. The excitation conditions for accurate one-hop neighbor inference are first derived with probability guarantees. Then, we extend the results to h-hop neighbor inference and multiple excitations cases, providing the explicit relationships between the inference accuracy and excitation magnitude. Specifically, the excitation based inference method is not only suitable for scenarios where abundant observations are unavailable, but also can be leveraged as auxiliary means to improve the accuracy of existing methods. Simulations are conducted to verify the analytical results.

关键词： Analytical models Network topology Topology Performance analysis Dynamical systems Testing

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Innovative Competency-Based Electrical engineering Curriculum 54

Innovative Competency-Based Electrical Engineering Curriculu...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Rehder, Gustavo P. Serrano, Ariana L. C. Grimoni, José B. Brandão, Anarosa A. F. Silva, Magno M. T. Moriya, Henrique T. Panazio, Cristiano M. Barbero, Edson Alayo, Marco I. Gianesi, Irineu G. N. Seabra, Antonio C. Universidade de São Paulo Electronic Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Electric Automation and Energy Dept. The Escola Politécnica São Paulo Brazil Universidade de São Paulo Computer Engineering and Digital Systems Department The Escola Politécnica São Paulo Brazil Universidade de São Paulo Telecommunications and Control Dept. The Escola Politécnica São Paulo Brazil Á lvares Penteado School of Commerce Foundation São Paulo Brazil Instituto Mauá de Tecnologia São Paulo Brazil

ISBN: (纸本)9798350351507

The Electrical engineering undergraduate program of the Escola Politécnica of the Universidade de São Paulo has created an alternative innovative curriculum path for its freshmen students in 2024. The Competency-based Path emphasizes the development of competencies and skills for the unprecedented technological advancements that necessitate a holistic education for future electrical engineers, preparing them with practical skills for real-world challenges. The program was designed using an innovative methodology and centers around semester-long or year-long integrative projects. These projects bridge the gap between theory and practice, as students deal with real-world challenges while fostering social responsibility. © 2024 IEEE.

关键词： Students

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Local Topology Inference of Mobile Robotic Networks under Formation control

arXiv

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arXiv 2022年

作者： Li, Yushan He, Jianping Cai, Lin Guan, Xinping The Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China The Dept. of Electrical and Computer Engineering University of Victoria BC Canada

The interaction topology is critical for efficient cooperation of mobile robotic networks (MRNs). We focus on the local topology inference problem of MRNs under formation control, where an inference robot with limited observation range can manoeuvre among the formation robots. This problem faces new challenges brought by the highly coupled influence of unobservable formation robots, inaccessible formation inputs, and unknown interaction range. The novel idea here is to advocate a range-shrink strategy to perfectly avoid the influence of unobservable robots while filtering the input. To that end, we develop consecutive algorithms to determine a feasible constant robot subset from the changing robot set within the observation range, and estimate the formation input and the interaction range. Then, an ordinary least squares based local topology estimator is designed with the previously inferred information. Resorting to the concentration measure, we prove the convergence rate and accuracy of the proposed estimator, taking the estimation errors of previous steps into account. Extensions on nonidentical observation slots and more complicated scenarios are also analyzed. Comprehensive simulation tests and method comparisons corroborate the theoretical findings. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Inferring Topology of Networked Dynamical Systems by Active Excitations

arXiv

引用

arXiv 2022年

作者： Li, Yushan He, Jianping Chen, Cailian Guan, Xinping Dept. of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

Topology inference for networked dynamical systems (NDSs) has received considerable attention in recent years. The majority of pioneering works have dealt with inferring the topology from abundant observations of NDSs, so as to approximate the real one asymptotically. Leveraging the characteristic that NDSs will react to various disturbances and the disturbance’s influence will consistently spread, this paper focuses on inferring the topology by a few active excitations. The key challenge is to distinguish different influences of system noises and excitations from the exhibited state deviations, where the influences will decay with time and the exciatation cannot be arbitrarily large. To practice, we propose a one-shot excitation based inference method to infer h-hop neighbors of a node. The excitation conditions for accurate one-hop neighbor inference are first derived with probability guarantees. Then, we extend the results to h-hop neighbor inference and multiple excitations cases, providing the explicit relationships between the inference accuracy and excitation magnitude. Specifically, the excitation based inference method is not only suitable for scenarios where abundant observations are unavailable, but also can be leveraged as auxiliary means to improve the accuracy of existing methods. Simulations are conducted to verify the analytical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Topology

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Predictability of Stochastic Dynamical System: A Probabilistic Perspective

Predictability of Stochastic Dynamical System: A Probabilist...

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IEEE Conference on Decision and control

作者： Tao Xu Jianping He Dept. of Automation Shanghai Jiao Tong University the Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, to characterize the predictability of discrete-time continuous-state stochastic dynamical system (SDS) and use predictability to explain the probability of making accurate predictions, we have proposed a new performance metric, i.e., predictability exponent. This metric quantifies the asymptotic exponential decaying rate of the probability that prediction errors never exceed a fixed ϵ. The novelties of this predictability metric lie in that it is proposed from an asymptotic probabilistic perspective, which i) minimizes the exponential decaying rate of prediction probability, ii) holds an approximated expression deeply related with differential entropy within an error of O(ϵ), and iii) possesses a fast converging speed from finite decaying rate of O(e −K ) in the sense of probability. Numerical examples are given to illustrate the efficiency of the obtained results.

关键词： Measurement Stochastic processes Predictive models Probabilistic logic Entropy Dynamical systems

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A review on task consolidation for cloud computing environment 2

A review on task consolidation for cloud computing environme...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Badr, Shaimaa El Mahalawy, Ahmed Attiya, Gamal Nasr, Aida A. Egyptian Electricity Transmission Company SCADA Engineer Middle Egypt Regional Control Center Egypt Engineering Menoufia University Faculty of Division Dept. of Computer Science and Engineering Menouf Egypt Tanta University Faculty of Computers and Information Egypt

ISBN: (纸本)9781665418423

Recently, Power Consumption is one of The Most Important Researches on Cloud Computing Systems such as CPU central Processors, Drives, memory, and temperature that affect on consuming the amount of energy. But also the amount of software approach has more effective on it, such as scheduling, resource allocation, and task *** Consolidation is an effective technique that is used to save power consumption which consolidates tasks by virtualization technologies. The goal of this optimizing is not only to minimize energy costs in cloud computing systems but also to respond to government and environmental rules. There are more than techniques to minimize energy consumption here we focus on the task consolidation technique and its important algorithms. In task consolidation, idle resources consume a major amount of energy consumption. Virtualization is an important part of the task consolidation *** this survey, we discuss how efficient task consolidation algorithms affect cloud resource power consumption. © 2021 IEEE.

关键词： Electric power utilization

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An Effective Classification of Ddos Cloud Based Attack Through Tree Founded Classifiers

An Effective Classification of Ddos Cloud Based Attack Throu...

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Vikas Goel Pragati Goel Raju Ranjan Amit Kumar Sharma Dept. of IT KIET Group of Institutions Delhi-NCR Ghaziabad India Dept. of Computer Application NCRD'S Sterling Institute of Management Studies Nerul Navi Mumbai India School of Computing Science & Engineering Galgotia University Greater Noida India Dept. of CS&E Ch. Charan Singh University Campus Meerut India

The term “cloud computing” refers to a relatively new technology that provides users with access to various computing services and resources, including software, data storage, and communication infrastructure. There are currently certain security concerns with cloud computing. An Intrusion Detection System (IDS) is a form of network security that monitors for malicious activity. In the cloud, an IDS might operate on individual hosts or over an entire network. DoS attacks, which aim to disrupt, disable, or otherwise harm a service, have become increasingly common against cloud applications and internet services. Due to the cloud's complexity and decentralized nature, it is difficult to detect attacks on its infrastructures. Moreover, by allowing a wide variety of smart devices to access cloud-based infrastructures, it raises both the difficulty and the complexity of assaults. A decision tree is a method of categorization used in data mining. It is recommended to utilize this method when developing a classification scheme from a reclassified data set. Classifiers such as the Classification and Regression Tree (CART), C4.5, and the Random Tree (RT) are proposed in this work. The utilized CART method has a tendency to generate additional trees on nodes, until no tree can be formed judging whether or not a node is terminal. Because of its adaptability, the CART technique can handle gaps in the data. The tree is pruned to make it smaller and more manageable in C4.5. A classifier's complexity is decreased while its prediction accuracy is improved. Decision trees were used to generate the chain of basic decisions used in RT's label assignment process.

关键词： computers Cloud computing Technological innovation Web and internet services Intrusion detection Signal processing Denial-of-service attack

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