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Deep Learning-Driven Anomaly Detection for IoMT-Based Smart Healthcare Systems

Computer Modeling in Engineering & Sciences 2024年第12期141卷 2121-2141页

作者： Attiya Khan Muhammad Rizwan ovidiu bagdasar Abdulatif Alabdulatif Sulaiman Alamro Abdullah Alnajim Department of Computer Science Kinnaird College for WomenLahore54000Pakistan School of Computing University of DerbyDerbyDE221GBUK Department of Mathematics Faculty of Exact Sciences“1 Decembrie 1918”University of Alba IuliaAlba Iulia510009Romania Department of Computer Science College of ComputerQassim UniversityBuraydah52571Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah52571Saudi Arabia

The Internet of Medical Things(IoMT)is an emerging technology that combines the Internet of Things(IoT)into the healthcare sector,which brings remarkable benefits to facilitate remote patient monitoring and reduce treatment *** IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to *** Detection Systems(IDS)play a crucial role in maintaining network *** IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal *** paper presents the development of an IDS based on deep learning techniques utilizing benchmark *** propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare *** primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security *** employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security *** proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive *** results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain.

关键词： Anomaly detection deep learning Internet of Things(IoT) health care

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O(t^(-β))-SYNCHRONIZATION AND ASYMPTOTIC SYNCHRONIZATION OF DELAYED FRACTIONAL ORDER NEURAL NETWORKS

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Acta Mathematica Scientia 2022年第4期42卷 1273-1292页

作者： Anbalagan PRATAP Ramachandran RAJA Jinde CAO Chuangxia HUANG Jehad ALZABUT ovidiu bagdasar Department of Mathematics Alagappa UniversityKaraikudi 630004India Ramanujan Centre for Higher Mathematics Alagappa UniversityKaraikudi 630004India School of Mathematics Southeast UniversityNanjing 211189China Yonsei Frontier Lab Yonsei UniversitySeoul03722South Korea Hunan Provincial Key Laboratory of Mathematical Modeling and Analysis in Engineering Changsha University of Science and TechnologyChangsha410114China Department of Mathematics and General Sciences Prince Sultan UniversityRiyadh 12435Saudi Arabia Department of Electronics Computing and MathematicsUniversity of DerbyUnited Kingdom

This article explores the O(t^(-β))synchronization and asymptotic synchronization for fractional order BAM neural networks(FBAMNNs)with discrete delays,distributed delays and non-identical *** designing a state feedback control law and a new kind of fractional order Lyapunov functional,a new set of algebraic sufficient conditions are derived to guarantee the O(t^(-β))Synchronization and asymptotic synchronization of the considered FBAMNNs model;this can easily be evaluated without using a MATLAB LMI control ***,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results.

关键词： O(t^(-β))-synchronization asymptotic synchronization BAM neural networks fractional order state feedback control law

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