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 tre...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 costs.As IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to cyberattacks.Intrusion Detection Systems(IDS)play a crucial role in maintaining network security.An IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal networks.This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets.We propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare domain.The primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security risks.We employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security framework.The proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive rate.Experimental results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain.展开更多
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 perturbations.By designing ...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 perturbations.By 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 toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results.展开更多
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support (QU-APC-2024-9/1).
文摘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 costs.As IoMT devices become more scalable,Smart Healthcare Systems(SHS)have become increasingly vulnerable to cyberattacks.Intrusion Detection Systems(IDS)play a crucial role in maintaining network security.An IDS monitors systems or networks for suspicious activities or potential threats,safeguarding internal networks.This paper presents the development of an IDS based on deep learning techniques utilizing benchmark datasets.We propose a multilayer perceptron-based framework for intrusion detection within the smart healthcare domain.The primary objective of our work is to protect smart healthcare devices and networks from malicious attacks and security risks.We employ the NSL-KDD and UNSW-NB15 intrusion detection datasets to evaluate our proposed security framework.The proposed framework achieved an accuracy of 95.0674%,surpassing that of comparable deep learning models in smart healthcare while also reducing the false positive rate.Experimental results indicate the feasibility of using a multilayer perceptron,achieving superior performance against cybersecurity threats in the smart healthcare domain.
基金joint financial support of Thailand Research Fund RSA 6280004,RUSA-Phase 2.0 Grant No.F 24-51/2014-UPolicy(TN Multi-Gen),Dept.of Edn.Govt.of India,UGC-SAP(DRS-I)Grant No.F.510/8/DRS-I/2016(SAP-I)+1 种基金DST(FIST-level I)657876570 Grant No.SR/FIST/MS-I/2018/17Prince Sultan University for funding this work through research group Nonlinear Analysis Methods in Applied Mathematics(NAMAM)group number RG-DES-2017-01-17。
文摘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 perturbations.By 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 toolbox.Finally,two numerical examples,along with the simulation results,illustrate the correctness and viability of the exhibited synchronization results.