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Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA) based Support Vector Machine (SVM) for Cyberattack Detection in Healthcare 2

Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA)...

引用

2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Alzubaidi, Laith H. Nijaguna, G.S. Pasha, Syed Nawaz Kotagi, Veeranna Kalaiselvi, K. The Islamic University Najaf Iraq S.E.A. College of Engineering and Technology Department of Information Science and Engineering Bangalore India Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ise Bengaluru India Hindusthan College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350317558

Usage of Internet of Things (IoT) in medical area is defined as IoMT that plays a signif-icant part in exchanging of sensitive information between medical systems. Though, that comes with privacy securities which comprised the security of information gathered through medical sensors, made them susceptible for potential cyber issues like modification of data, attacks re-play. These attacks can cause important information loss or unauthorized modifications. In this research, a Gaussian Bare-Bones Butterfly Optimization Algorithm (GBBOA) based Support Vector Machine (SVM) is proposed for cyberattack detection. The dataset used in research is WUSTL-EHMS-2020 which is healthcare dataset. The dataset is pre-processed through label encoding and data normalization. Then feature extraction is performed by Kernal Partial Least Square (KPLS) Method and detection is performed through GBBOA based SVM. The perfor-mance of proposed method is analysed with performance measures of accuracy, precision, re-call and f1-score. The proposed method attained accuracy 97.72%, precision 97.03%, recall 96.56% and f1-score 96.84% which is efficient than other existing methods like Particle Swarm Optimization - Machine learning (PSO - ML) and ensemble Machine Learning (ML) methods. © 2024 IEEE.

关键词： Cyber-attacks Gaussian Bare-Bones Butterfly Optimization Algorithm Internet of Medical Things Kernal Partial Least Square and Support Vector Machine

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Blockchain-Based Federated Learning-Convolutional Neural Network for Preserving Data Privacy and Security

Blockchain-Based Federated Learning-Convolutional Neural Net...

引用

2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Manoj Kumar, D.P. Ananda Babu, J. Selva Lakshmi, C.B. Fatima, Tabeen Rajesh, N. Kalpataru Institute of Technology Department of Computer Science and Engineering Tiptur India Malnad College of Engineering Department of Information Science and Engineering Hassan India Velammal College of Engineering and Technology Department of Cse India Cmr Technical Campus Department of Computer Science and Engineering Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The Internet of Things (IoT) and Industrial cloud computing have completely transferred the healthcare sector due to the rapid rise of distributed healthcare data. The security and privacy of healthcare data are critical issues facing in the healthcare sector. In this research, a BlockChain-based Federated Learning-Convolutional Neural Network (BC-FL-CNN) is proposed for preserving electronic health data privacy by integrating the BC and Deep Learning (DL) approach. CNN is used to classify normal and abnormal users in the processed dataset. The abnormal users were then processed and removed from the database as well as access to the health data utilizing BC with FL approach. Existing methods such as Convolution Neural Network-BlockChain-Cryptography-Federated Learning (CNN-BC-Cryp-FL), FL, and FL Risk-based Authorization Middleware for Healthcare (FRAMH) are used to compare with the proposed BC-FL-CNN approach. The proposed BC-FL-CNN achieves better accuracy of 98.25%, precision of 97.23%, recall of 97.56%, and 97.16% f1-score when compared to the existing approaches like CNN-BC-Cryp-FL, FL, and FRAMH respectively. © 2023 IEEE.

关键词： BlockChain Federated Learning Healthcare data Internet of Things Privacy Security

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Observing and Predicting Land Slide using Three Stage Warning System in WSN 4

Observing and Predicting Land Slide using Three Stage Warnin...

引用

4th International Conference on electronics and Sustainable communication Systems, ICESC 2023

作者： Akila, T. Vasanth, P. Kalaiselvi, S. Nalini, M. Mahendra College of Engineering Department of Information Technology Tamil Nadu Salem India Sri Venkateswara College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India Kongu Engineering College Department of Computer Technology Tamil Nadu Erode India S. A. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300093

A landslide is an environmental disaster that originates from humans' enormous destruction of infrastructure. Real-time observing of a landslide lying region is essential to the problem for warning. A wireless sensor network (WSN) is an effective technique for observing real-time disaster-prone areas. To achieve real-time observation, an enormous amount of information must be gathered and examined in a short time. This approach has been introduced for efficient information gathering by the threshold warning stage. This approach proposes Observing and Predicting Land Slides (OPLS) using three Stage Warning System in WSN. Apply a three-stage warning system that decides the amount of information gathered and prioritizes the average and high-level warning message. The initial stage warning stage is a low-level alert, and the average stage warning system contains a medium-level warning system. In addition, the high-stage warning system represents the high-level alert message. Use sleep and awake scheduling to minimize the network's energy consumption in this approach. It is an important message, and this message evades high-level destruction. The queue will prioritize the high-level alert message to avoid the time delay. The experimental outcomes demonstrate that the OPLS method minimizes the packet loss ratio, and the sensor node provides better sensing results in better accuracy in the WSN. © 2023 IEEE.

关键词： Energy utilization

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Optimizing User Recommendations with Variational Autoencoders: Insights from MovieLens-1M and BookCrossing Datasets

Optimizing User Recommendations with Variational Autoencoder...

引用

computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kumari Samridhi Bam Bahadur Sinha Trinanjan Das Electronics and Communication Engineering Indian Institute of Information Technology Ranchi India Computer Science and Engineering National Institute of Technology Sikkim India Computer Science and Engineering Indian Institute of Information Technology Ranchi India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Recommender systems utilize algorithms and data to predict user preferences based on their past choices. While these systems can be highly accurate, this increased accuracy can sometimes lead to predictability and monotony. In the realm of big data, recommender systems are both prevalent and valuable. Research on these systems has intensified in recent years, accompanied by a growing number of datasets. This paper examines the application of Variational Autoencoder (VAE)-based recommender systems using the MovieLens 1M and BookCrossing datasets. The study primarily evaluates the performance of the VAE model using Root Mean Square Error (RMSE) as the key metric. The VAE model was trained on these datasets to predict user preferences and generate personalized recommendations. Experimental results revealed an RMSE of less than 1 for both datasets, indicating the model’s proficiency in accurately estimating user ratings for items. These findings suggest that VAE-based recommender systems are promising for enhancing recommendation accuracy and personalization. This research contributes to the understanding of VAE models within the context of recommender systems and provides insights for future research and potential improvements in this field.

关键词： Measurement computer vision Accuracy Mean square error methods Predictive models Prediction algorithms Root mean square Recommender systems Machine intelligence Context modeling

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Reconfigurable Intelligent Surface as an Access Point: Probability of Error Analysis for Discrete Phase Shifters 2nd

Reconfigurable Intelligent Surface as an Access Point: Pro...

引用

2nd International Conference on Computational electronics for Wireless communications, ICCWC 2022

作者： Anjana, B.S. Kumaravelu, Vinoth Babu Jadhav, Hindavi Murugadass, Arthi Sayeed, Md. Shohel Alhadidi, Basim Department of Communication Engineering School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Vellore India Sreenivasa Institute of Technology and Management Studies Andhra Pradesh Chittoor India Multimedia University Melaka Malaysia Department of Computer Information Systems Prince Abullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789811966606

A fresh line of research for future wireless networks is called reconfigurable intelligent surface (RIS)-aided communication. We developed one-bit and two-bit discrete phase shifter facilitated RIS for access point (AP) scenario in this study. In the AP scenario, RIS is considered as a source and it can transmit the information. When RIS is utilized as an AP, it can generate the phase angles needed for constellation mapping as well as compensate for channel distortion. The proximity of the radio frequency (RF) signal source to RIS minimizes the impact of fading between RIS and AP. The exact phase compensation of each RIS component is very difficult practically. Consequently, we can enhance the system’s overall performance using a discrete phase shifter. We are evaluating the effectiveness of one- and two-bit discrete phase shifters over intelligent RIS-AP and blind RIS-AP methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Access point (AP) Blind communication Discrete phase shifters Intelligent communication Reconfigurable intelligent surfaces (RIS)

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Quantum Inspired Marine Predator Algorithm with Long Short-Term Memory for Detection and Classification of Chronic Kidney Disease 2

Quantum Inspired Marine Predator Algorithm with Long Short-T...

引用

2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Srikanth, T. Mohamed, Hassan Jayaprakash, D. Vasanthakumar, G.U. Vijayakumari, G. Maisammaguda Hyderabad India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Information Technology Sona College of Technology Salem India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Electronics and Communication Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (纸本)9798331504960

Chronic Kidney Disease (CKD) is a continual decline of the kidney working time being and it is a progressive condition. The main problem with CKD is, it is asymptomatic in its early stages, this makes the early detection challenging. In order to detect the symptoms of the disease and to figure out the exact stage of the disease advance technologies have been introduced. The traditional methods failed to diagnosing the disease accurately in early stage. The paper proposed a Quantum Inspired Marine Predator Algorithm (QIMPA) in order to predict and classify the chronic kidney disease. This algorithm is employed with the marine predators using Brownian and Levy flights that uses hunting strategies. This strategy works in three phases, firstly, prey’s movements and strategies are noted how it escapes from the predator, then a balanced approach is created between predator and prey. Finally, in this phase, optimization process produces a Levy flight pattern to capture the predator and prey moves at low velocity. Long Short-Term Memory (LSTM) is utilized for the classification, it works by capturing the long-range values of features that enhances the diagnosing kidney disease. The proposed QIMPA model obtained 99.04% for CKD dataset compared to the existing methods. © 2024 IEEE.

关键词： Diseases

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An Intelligent Agent Based Optimal Scheduling & Congestion Free Routing Scheme to Enhance QoS of NFV Enabled Heterogeneous Wireless Sensor Network Framework

An Intelligent Agent Based Optimal Scheduling & Congestion F...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Prathap C S Sivaranjani Sathya M Prabhakaran G Rajalakshmi J Kavin Kumar K GokulChandrasekaran Department of Computer Science and Engineering NadarSaraswathi College of Engineering and Technology Theni Department of Computer Science and Engineering SethuInstituteofTechnology Virudhnagar Department of Information Technology Nadar Saraswathi College of Engineering and Technology Theni Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Electronics and Communication Engineering Sethu Institute of Technology Virudhunagar Department of Electronics and Communication Engineering Kongu Engineering College Perundurai Erode Department of Electronics and Communication Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In wireless sensor network (WSN), congestion is regarded as an unavoidable issue as of today’s scenario at which the data traffic is increased to their aggregated channel capacity. This results in the consequence of buffer overflowing which leads to packets drop thereby reducing packet delivery ratio and degrades network throughput. Also, routing is the most favored approaches for the minimization of nodes energy consumption and the enhancement of Heterogeneous WSNs throughput. Since, the problem of routing has been proved to be an NP-hard, it has been realized that optimization-based technique offers better performance. For solving these issues, this paper proposes a congestion free optimal routing scheme based on Genetic algorithm-based distance aware-Low Energy Adaptive Clustering Hierarchy (GADA-LEACH) protocol to predict intelligent agent path. The major intention of this work is to enhance the efficiency of energy and QoS (throughput and delay) in edge-cloud based IoT environment of data transmission. Initially, the system model is initialized and the graph construction is made for the process of clustering using Spectral graph theory (SGT) at which the cluster head (CH) selection is made. Then, the scheduling process is carried by means of Packet weighted based ensemble optimization scheduling (PW-EOS) scheme. The NFV enabled optimal edge node is selected using Reconfigured Cuckoo Search optimizer (RCSO) after which the intelligent agent moving path in the Heterogeneous Network (HetNet) are predicted by GADA-LEACH technique. From this, an effective routing path is determined among CH and an optimized Edge node. Eventually, the performance assessment is carried in terms of throughput, energy consumption, survival rate, PDR, End-to-End delay and the outcomes conquered are compared with existing methods to attest the proficiency of proposed scheme.

关键词： Wireless sensor networks Energy consumption Protocols Quality of service Optimal scheduling Routing Throughput Scheduling Delays Intelligent agents

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Range-angle Imaging Based on the Prior of Target Scattering Characteristics in Distributed Arrays 2

Range-angle Imaging Based on the Prior of Target Scattering ...

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2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Mei, Yongjie Zhang, Guangbin Huang, Tianyao Wang, Yan Beijng Institute of Technology School of Information and Electronics Beijing China Yangtze Delta Region Academy Beijing Institute of Technology Radar System and Intelligent Signal Processing Jiaxing China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China

ISBN: (纸本)9798331515669

We consider the problem of target range-angle imaging using distributed arrays. High resolution imaging requires a large array aperture. However, due to the limited number of array elements, the subarrays are usually sparsely located, which results in severe under-sampling and ambiguity problem, degrading the imaging performance. To this end, we propose a range-angle imaging method based on the prior of target scattering characteristic to resolve the ambiguity. The main idea is to use the information increment of priors to reduce the dependence on the measurements. Particularly, we first statistically extract the prior distribution of target scattering intensity by data analysis on the target images. Then we use these priors to design weighting parameters and achieve the ambiguity-free images by solving a weighted sparse recovery problem. Finally, numerical simulation results verify the feasibility of the proposed method. © 2024 IEEE.

关键词： ambiguity distributed array Range-angle imaging the prior of target scattering characteristics weight

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Cyber Defence Based on Artificial Intelligence and Neural Network Model in Cybersecurity 8

Cyber Defence Based on Artificial Intelligence and Neural Ne...

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8th IEEE International Conference on Science, Technology, engineering and Mathematics, ICONSTEM 2023

作者： Sugumaran, D. Mahaboob John, Y.M. Mary C, Jansi Sophia Joshi, Kireet Manikandan, G. Jakka, Geethamanikanta Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Information Technology Tamil Nadu Chennai India Mahendra College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Salem India Idhaya Engineering College for Women Department of Computer Science and Engineering Tamilnadu Kallakurichi India Graphic Era Deemed to Be University Department of Computer Science Engineering Uttrakhand Dehradun India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Thandalam India University of the Cumberlands Department of Information Technology FremontCA United States

ISBN: (纸本)9798350347791

Cybersecurity is an increasingly critical aspect of modern society, with cyber attacks becoming more sophisticated and frequent. Artificial intelligence (AI) and neural network models have emerged as promising tools for improving cyber defense. This paper explores the potential of AI and neural network models in cybersecurity, focusing on their applications in intrusion detection, malware detection, and vulnerability analysis. Intruder detection, or "intrusion detection,"is the process of identifying Invasion of Privacy to a computer system. AI-based security systems that can spot intrusions (IDS) use AI-powered packet-level network traffic analysis and intrusion detection patterns to signify an assault. Neural network models can also be used to improve IDS accuracy by modeling the behavior of legitimate users and detecting anomalies. Malware detection involves identifying malicious software on a computer system. AI-based malware machine-learning algorithms are used by detecting systems to assess the behavior of software and recognize patterns that indicate malicious activity. Neural network models can also serve to hone the precision of malware identification by modeling the behavior of known malware and identifying new *** analysis involves identifying weaknesses in a computer system that could be exploited by attackers. AI-based vulnerability analysis systems use machine learning algorithms to analyze system configurations and identify potential vulnerabilities. Neural network models can also be used to improve the accuracy of vulnerability analysis by modeling the behavior of known vulnerabilities and identifying new ***, AI and neural network models have significant potential in cybersecurity. By improving intrusion detection, malware detection, and vulnerability analysis, they can help organizations better defend against cyber attacks. However, these technologies also present challenges, including a lack of understanding of the impo

关键词： Neural network models

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Integrity of Code and IoT Validation of Resource Utilization in Micro Control Unit

Integrity of Code and IoT Validation of Resource Utilization...

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2023 International Conference on New Frontiers in communication, Automation, Management and Security, ICCAMS 2023

作者： Kushwaha, Ritesh Kumar Ravikumar, Parimi Lvd Mohammed, Muzammil H Sasthi Kumar, C. Chandigarh University Department of Electronics & Communication Engineering Punjab Mohali India Shri Vishnu Engineering College for Women Deparmnent of Information Technology Bhimavaram India Taif University Department of Information Technology Taif Saudi Arabia Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798350317060

Cybersecurity for the Internet of Things is gaining more and more attention as the number of IoT devices has increased and the number of attack stargeting these devices continues to grow exponentially. However, the significant focus areas for security measures have been guaranteeing services' constant availability and protecting users' private data. But code integrity is not receiving the attention it deserves because these devices lack the horsepower to provide more robust safeguards. This issue necessitates more study into adapting standard procedures for code integrity to IoT gadgets. This paper describes a virtual machine-based technique for ensuring the correct execution of code on the Internet of Things. The feasibility of a virtual machine tailored to safeguard software during firmware upgrades is demonstrated here. The suggested method's low resource requirements on popular microcontrollers have been verified experimentally. The acquired findings prove that this secured technique for coding may work in practice. © 2023 IEEE.

关键词： Internet of things

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