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28th IEEE Symposium on computers and Communications, ISCC 2023

作者： Tang, Zhimin Ma, Duohe Sun, Xiaoyan Chen, Kai Wang, Liming Jiang, Junye Institute of Information Engineering Chinese Academy of Sciences Beijing China California State University Department of Computer Science Sacramento United States School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350300482

Traditional defense methods are hard to change the inherent vulnerabilities of static data storage, single data access, and deterministic data content, leading to frequent data leakage incidents. Moving target defense (MTD) techniques can increase data diversity and unpredictability by dynamically shifting the data attack surface. However, in the existing methods, the data lacks sufficient dynamics due to insufficient shifting space and shifting frequency of attack surface, and legitimate users are inevitably greatly affected. This study proposes a data MTD method that the data changes dynamically based on real-Time multi-source user access information. Through the multidimensional user stratification mechanism, we establish a novel dynamic data model that uses the combination of random deception strategies to convert metadata properties and content of data based on the user risk levels, while data remains unchanged for legitimate users. Multiple sets of experiments demonstrate the effectiveness and low consumption of our data dynamic defense approach. © 2023 IEEE.

关键词： Digital storage

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Diversified Grid Optimization Model to Handle Fast Charging Provision of Electric Vehicles Using Artificial Intelligence (AI) Technique

Diversified Grid Optimization Model to Handle Fast Charging ...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Karthik, G. Kumar, R.Sampath Manimegalai, L. Rajkumar, G.Vinoth Adkane, R.V. Odeh, Mahmoud Department of Computer Science and Engineering Salem India Kpr institute of Engineering and Technology Department of Mechatronics Engineering Coimbatore India R.M.K. College of Engineering and Technology Department of Electronics and Communication Chennai India J.P College of Engineering Department of Electronics and Communication Engineering Tenkasi India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Secuity Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The transportation industry is taking a giant leap forward towards a dependable, carbon-neutral future thanks to electric vehicles (EVs). The growing number of electric vehicles on the road is putting a strain on power networks and electrical grids caused by dispersed energy resources, particularly the EVI's demands for charging and discharging. Without proper coordination of charging and discharging, the already overburdened power distribution system would struggle to handle the large number of electric vehicles on the road. To get more people to use scheduling programs, electric vehicle owners can use dynamic pricing, a kind of demand response. Studying the timing of electric vehicle charging and discharging as well as its dynamic pricing model is, hence, crucial. There has been a lot of study on scheduling and demand forecasting models for electric vehicle charging that employ artificial intelligence, with many academics claiming that AI approaches outperform more traditional optimization techniques. The Diversified Grid Optimization Model (DGOM) is a novel method for optimizing charging facilities that aims to provide fast and efficient charging. To test its efficacy, it is cross-validated with the Dynamic Vehicle Charging Model (DVCM), a traditional charging scheme. The investigations pertaining to electric vehicle charging and discharging are categorized into scheduling, pricing, and forecasting in this paper's experimental analysis. This work finds the missing pieces of the puzzle when it comes to dynamic pricing models and EV discharge scheduling by establishing the connection between forecasting, scheduling, and pricing mechanisms. © 2024 IEEE.

关键词： State of charge

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A Novel Two-Stage Feature Selection Framework for Efficiently Diagnosing Alzheimer's Disease 7

A Novel Two-Stage Feature Selection Framework for Efficientl...

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7th International Conference on Data Science and Information technology, DSIT 2024

作者： Lu, Pinya Gao, Wenzhao Pan, Jie Yang, Hongqin Ding, Xuemei Fujian Normal University College of Computer and Cyber Security Fuzhou China Ulster University School of Computing Engineering & Intelligent Systems NI Derry-Londonderry United Kingdom Xiamen Jingyi Zhikang Technology Company Xiamen China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou China

ISBN: (纸本)9798350384093

Research focus on the trade-off between diagnostic accuracy for Alzheimer's disease (AD) and time management for diagnosis is very limited. This study proposes a novel two-stage feature selection framework integrating variance inflation factor (VIF) and NSGA-II optimization algorithm to obtain a set of sub-items of cognitive and neuropsychological assessments (CNAs) with high diagnostic accuracy and short managing time. What sets the proposed framework apart is that it 1) mitigates multicollinearity issues caused by multiple types of feature selection algorithms employed on multiple CNAs and 2) has the multi-objective optimization capacity of searching Pareto solutions in a reduced decision space. Crucially, we design a VIF-based fast forward search for feature selection which takes into account the cost of managing time for CNAs. Experimental results demonstrate a significantly shortened managing time of 905 seconds (reduced by two-third), with a slightly improved AUC performance of 0.9113 for classifying cognitively normal controls, mild cognitive impairments, and AD patients based on a set of selected combination of sub-items compared to their involved full CNAs, thereby offering promising prospects for accurate diagnosis and fast assessment in clinical practice. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Hermes - Smart College Application using Android

Hermes - Smart College Application using Android

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： U.M. Fernandes Dimlo M SilpaRaj Ch. Srilakshmi DBK Kamesh Allam Balaram Department of CSE Sreyas Institute of Engineering and Technology Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Department of IT VNR Vignana Jyothi Institute of Enginnering and Technology Hyderabad Department of CSE Malla Reddy Engineering College for Women Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Android apps emerged with modern technology. It's time to ditch websites for apps. We are thrilled to debut the Smart-College app, which will act like a condensed college website. The teaching staff and parents of students in their classrooms will use this app to track attendance, grades, and campus club activity. This tool helps colleges, students, and parents communicate more closely. Logging in with their credentials lets parents track their child's progress via the app. Teachers can record attendance using the programme, and if a student is absent, parents' mobile devices will be notified promptly. This project aims to reduce parents' academic responsibilities. This app was developed in Android Studios and deployed on AWS Server.

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Utilizing Smartwatches and Deep Learning Models for Enhanced Avalanche Victim Identification, Localization, and Efficient Recovery Strategies: An In-depth Study 23

Utilizing Smartwatches and Deep Learning Models for Enhanced...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Khyati Jha Mansi Pasbola Hari Mohan Rai Saule Amanzholova Department of Computer Science Dronacharya Group of Institutions India Faculty of Computer Technologies and Cyber Security International University of Information Technology Kazakhstan

ISBN: (纸本)9798400709418

This article explores the transformative impact of modern technology on avalanche response and safety, addressing the longstanding challenges associated with avalanches in mountainous regions. Historically, avalanches have presented significant dangers, compounded by the difficulties of conducting rescue operations in remote and harsh environments. However, recent technological advancements, including smartwatches with SOS features and specialized mobile applications, have ushered in a new era of avalanche safety. Smartwatches have emerged as crucial tools in avalanche safety. Equipped with SOS features and synchronized with mobile apps, these devices have revolutionized the way we approach avalanche emergencies. In this study, we propose a methodology that leverages smartwatch data and deep learning models to detect individuals buried under avalanche debris. This approach aims to significantly improve the chances of successful and timely rescue operations. The proposed methodology involves collecting data from smartwatches, which are equipped with various sensors to monitor location, movement, and environmental conditions. Deep learning models are then deployed to analyze this data, identifying patterns and anomalies associated with avalanche incidents. By harnessing these advanced technologies, our methodology offers the potential for real-time avalanche detection, thereby enhancing overall safety outcomes. One of the key strengths of this methodology is its adaptability to diverse avalanche scenarios. The study envisions a future where technology-driven avalanche safety becomes an integral component of outdoor activities, mitigating risks and enabling more effective response strategies in the face of this natural hazard.

关键词： Avalanche Beacons

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Deepfake Video Detection: Analysis for Deep Learning models using Transfer Learning

Deepfake Video Detection: Analysis for Deep Learning models ...

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Information technology (ACIT)

作者： Shahad Altamimi Walid Salameh Cyber Security Department Princess Sumaya University for Technology Amman Jordan Computer Science Department Princess Sumaya University for Technology Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The unchecked spread of deepfake videos jeopardizes public trust, national security, and media credibility. The capacity to recognize falsified videos is critical for protecting privacy and combatting misinformation. The article seeks to illustrate the usage of Deep Learning (DL) approaches for recognizing deepfake videos, including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and an RNN-LSTM combination with Transfer Learning. The research used the LSTM model to examine the efficacy of temporal correlations between video frames in the Deepfake Detection Challenge (DFDC) dataset of deep fake videos, and it claimed complete success in both the training and testing stages. In addition, the study used frame level masks and characteristics that were created throughout the research's pre-processing steps. The findings show that the model had a train loss of 0.021 and a test loss of 0.813, suggesting that it was extremely resilient. Other crucial indicator measures, such as Mean Squared Error (MSE) and Structural Similarity Index Measure (SSIM), aid in evaluating the model's performance and validate its high accuracy in recognizing deepfake material. This study is very important since it greatly contributes to the body of knowledge in the early phases of deepfake identification and aids in the mitigation of the risks connected with manipulated media.

关键词： Deep learning Deepfakes Accuracy Recurrent neural networks Measurement uncertainty Transfer learning Mean square error methods Data models Indexes Long short term memory

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Experimental Evaluation of an Improved Air Quality Prediction Approach Based on AI Powered Deep Learning Mechanism

Experimental Evaluation of an Improved Air Quality Predictio...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Ranjithkumar, J. Malarvizhi, C. Porkodi, M. Vidhyasree, V. Neeraj, S. Al-Mousa, M.R. Jeppiaar Engineering College Department of Ece Chennai India Rajalakshmi Institute of Technology Department of Ece Chennai India Sona College of Technology Department of Eee Salem India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India College of Information Technology Zarqa University Department of Cyber Security Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Everyone knows that pollution is becoming worse and worse, and that this is contributing to a sharp increase in global mortality rates;nevertheless, 'Air-Pollution' is particularly harmful to humans. Air pollution is rapidly becoming a big threat to all forms of life on Earth, including humans. Officials in charge of public health are worried about the steadily declining air quality in our nation. Pollution levels are rising, which is causing the air quality index to rise daily. Air pollution not only adds to climate change, but it may also cause lung cancer, neurological diseases, and the greenhouse effect, all of which can be lethal. Reducing air pollution requires accurate air quality forecasts. Predicting the air quality index makes use of Machine Learning and Deep Learning techniques, despite the abundance of research on the subject. An improved learning for quality prediction strategy, or ILQPS, is developed in this research as a new deep learning model for detecting air quality contamination. In order to assess the efficacy of the suggested system, this model is cross-validated with the traditional learning model known as Linear Regression (LR). Supporting early warning and regional pollution control, the suggested solutions can greatly enhance public access to information on air quality forecast. © 2024 IEEE.

关键词： Greenhouse effect

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A Secure and Lightweight Chaos Based Image Encryption Scheme

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computers, Materials & Continua 2022年第10期73卷 279-294页

作者： Fadia Ali Khan Jameel Ahmed Fehaid Alqahtani Suliman A.Alsuhibany Fawad Ahmed Jawad Ahmad Department of Electrical Engineering Riphah International UniversityIslamabad44000Pakistan Department of Computer Science King Fahad Naval AcademyAl Jubail35512Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Cyber Security Pakistan Navy Engineering CollegeNUSTKarachi 75350Pakistan School of Computing Edinburgh Napier UniversityEdinburghEH105DTUnited Kingdom

In this paper,we present an image encryption scheme based on the multi-stage chaos-based image encryption *** method works on the principle of confusion and *** proposed scheme containing both confusion and diffusion modules are highly secure and effective as compared to the existing ***,an image(red,green,and blue components)is partitioned into blocks with an equal number of *** block is then processed with Tinkerbell Chaotic Map(TBCM)to get shuffled pixels and shuffled *** Fractal Function(CFF)change the value of pixels of each color component(layer)to obtain a random *** the obtained random sequence,three layers of plain image are ***,with each encrypted layer,Brownian Particles(BP)are XORed that added an extra layer of *** experimental tests including a number of statistical tests validated the security of the presented *** results reported in the paper show that the proposed scheme has higher security and is lightweight as compared to state-of-the-art methods proposed in the literature.

关键词： Chaos fractals fibonacci tinkerbell chaotic map confusion diffusion brownian motion

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Total Harmonics Distortion Prediction at the Point of Common Coupling of industrial load with the grid using Artificial Neural Network

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Energy and AI 2023年第4期14卷 294-303页

作者： Emenike Ugwuagbo Adeola Balogun Biplob Ray Adnan Anwar Chikodili Ugwuishiwu Centre for Space Transport and Propulsion (CSTP)Epe1 LASU Epe driveLagos106101Nigeria Department of Electrical&Electronics Engineering University of LagosAkoka101017LagosNigeria Centre for Intelligent Systems(CIS) School of Engineering and TechnologyCQ UniversityNorman Gardens4701RockhamptonAustralia Strategic Centre for Cyber Security Research Institute(CSRI) School of Information TechnologyDeakin UniversityGeelong3216MelbourneAustralia Department of Computer Science University of NigeriaNsukka410001EnuguNigeria

Power quality challenges have generated a lot of disputes between utilities,customers,network operators,and equipment manufacturers around the world as regards the share of responsibility for power quality solutions,this results in different levels of financial and technical losses for both the network operators and the *** of the major consequences of the operation of heavy-duty factories globally is the corruption of power quality at the point of common coupling(PCC).In order to quantify the harmonics contribution at the PCC by industrial consumers,this paper presents three-phase total harmonics distortion of current(THDi)prediction model at the *** proposed artificial neural network(ANN)models use a multilayer perceptron neural network(MLPN)to predict three-phase total harmonic *** input parameter used in the models is easily measured with basic power *** model was trained with input parameters captured at 33 kV and 132 kV voltage levels using power quality meters at five(5)different steel manufacturing ***(8)different models were designed,trained,validated,and tested with different combinations of input parameters,number of hidden layers,and number of neurons in the hidden *** results show that the model with two hidden layers which uses four major power parameters(Current,apparent power,reactive and active power)as input parameters in the training model had the best performance with a 95.5%coefficient of correlation between the measured THDi and the predicted THDi.

关键词： Total harmonics distortion(THD) Power quality(PQ) Artificial neural network(ANN)

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A security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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