Securing the Internet of Health Things: Embedded Federated Learning-Driven Long Short-Term Memory for Cyberattack Detection DOI Open Access

Manish Kumar,

Sunggon Kim

Electronics, Journal Year: 2024, Volume and Issue: 13(17), P. 3461 - 3461

Published: Aug. 31, 2024

The proliferation of the Internet Health Things (IoHT) introduces significant benefits for healthcare through enhanced connectivity and data-driven insights, but it also presents substantial cybersecurity challenges. Protecting sensitive health data from cyberattacks is critical. This paper proposes a novel approach detecting in IoHT environments using Federated Learning (FL) framework integrated with Long Short-Term Memory (LSTM) networks. FL paradigm ensures privacy by allowing individual devices to collaboratively train global model without sharing local data, thereby maintaining patient confidentiality. LSTM networks, known their effectiveness handling time-series are employed capture analyze temporal patterns indicative cyberthreats. Our proposed system uses an embedded feature selection technique that minimizes computational complexity cyberattack detection leverages decentralized nature create robust scalable mechanism. We refer as Embedded Learning-Driven (EFL-LSTM). Extensive experiments real-world ECU-IoHT demonstrate our outperforms traditional models regarding accuracy (97.16%) privacy. outcomes highlight feasibility advantages integrating networks enhance posture infrastructures. research paves way future developments secure privacy-preserving systems, ensuring reliable protection against evolving

Language: Английский

Enhancing Internet of Medical Things security with artificial intelligence: A comprehensive review DOI Creative Commons
Sotiris Messinis, Nikos Temenos, Nicholas Ε. Protonotarios

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 170, P. 108036 - 108036

Published: Jan. 28, 2024

Over the past five years, interest in literature regarding security of Internet Medical Things (IoMT) has increased. Due to enhanced interconnectedness IoMT devices, their susceptibility cyber-attacks proportionally escalated. Motivated by promising potential AI-related technologies improve certain cybersecurity measures, we present a comprehensive review this emerging field. In review, attempt bridge corresponding gap modern that deploy AI techniques performance and compensate for privacy vulnerabilities. direction, have systematically gathered classified extensive research on topic. Our findings highlight fact integration machine learning (ML) deep (DL) improves both measures speed, reliability, effectiveness. This may be proven useful improving devices. Furthermore, considering numerous advantages as opposed core counterparts, including blockchain, anomaly detection, homomorphic encryption, differential privacy, federated learning, so on, provide structured overview current scientific trends. We conclude with considerations future research, emphasizing AI-driven landscape, especially patient data protection data-driven healthcare.

Language: Английский

Citations

26
On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives DOI
Anichur Rahman, Kamrul Hasan,

Dipanjali Kundu

et al.

Future Generation Computer Systems, Journal Year: 2022, Volume and Issue: 138, P. 61 - 88

Published: Aug. 17, 2022

Language: Английский

Citations

69
Federated Learning for the Internet-of-Medical-Things: A Survey DOI Creative Commons
Vivek Kumar Prasad, Pronaya Bhattacharya, Darshil Maru

et al.

Mathematics, Journal Year: 2022, Volume and Issue: 11(1), P. 151 - 151

Published: Dec. 28, 2022

Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital driving analytics (HA) toward extracting meaningful data-driven insights. concerns raised over sharing IoMT, stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, model deemed inaccurate. a transformative shift has started HA centralized learning paradigms towards distributed edge-learning paradigms. In learning, federated (FL) allows for training on local without explicit data-sharing requirements. However, FL suffers high degree of statistical heterogeneity models, level partitions, fragmentation, which jeopardizes its accuracy during updating process. Recent surveys yet discuss challenges massive datasets, sparsification, scalability concerns. Because this gap, survey highlights potential integration aggregation policies, reference architecture, use models support A case study trusted cross-cluster-based FL, Cross-FL, presented, highlighting gradient policy remotely networked hospitals. Performance analysis conducted regarding system latency, accuracy, trust consensus mechanism. The outperforms approaches by margin, makes it viable real-IoMT prototypes. As outcomes, proposed addresses key solutions organizations.

Language: Английский

Citations

39
Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks DOI Open Access
Izhar Ahmed Khan, Imran Razzak, Dechang Pi

et al.

Information Fusion, Journal Year: 2023, Volume and Issue: 101, P. 102002 - 102002

Published: Sept. 2, 2023

Language: Английский

Citations

35
A survey on federated learning for security and privacy in healthcare applications DOI
Kristtopher K. Coelho, Michele Nogueira, Alex Borges Vieira

et al.

Computer Communications, Journal Year: 2023, Volume and Issue: 207, P. 113 - 127

Published: May 19, 2023

Language: Английский

Citations

24
A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems DOI Creative Commons
Muhammad Asad,

Saima Shaukat,

Ehsan Javanmardi

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(10), P. 6201 - 6201

Published: May 18, 2023

Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development the use of federated learning for recommendation systems (FRSs). An FRS provides way protect user privacy by training models using intermediate parameters instead real data. This approach allows cooperation between platforms while still complying with regulations. In this paper, we explored current state research on FRSs, highlighting existing issues possible solutions. Specifically, looked at how FRSs can be used allowing organizations benefit from they share. Additionally, examined potential applications in context big data, exploring these facilitate secure sharing collaboration. Finally, discuss challenges associated developing deploying world addressed.

Language: Английский

Citations

24
IoT-Enabled Secure and Intelligent Smart Healthcare DOI
Wasswa Shafik

Advances in computational intelligence and robotics book series, Journal Year: 2024, Volume and Issue: unknown, P. 308 - 333

Published: April 1, 2024

This study examines the complex array of impediments and potential advantages internet things (IoT)-enabled secure intelligent smart healthcare devices (IESISHDs) associated with shift towards enabling cities, motivated by pressing necessity to address climate change promote sustaining systems. looks at technological, economic, social problems that need be solved in order make cities smarter IoT. It does this reading a lot scholarly sources. Most stupendously, it emphasizes environmentally sustainable merits, for economic growth, improvements societal well-being can arise from transition. further depicts selected case studies demonstrate empirical evidence provide policy recommendations. The paradigm is assist governments other stakeholders effectively managing human-associated challenges attain maximum value an innovative future guarantees worldwide prosperity ecological welfare.

Language: Английский

Citations

9
Efficient Load Balancing for Blockchain-Based Healthcare System in Smart Cities DOI Creative Commons

Faheem Nawaz Tareen,

Ahmad Naseem Alvi,

Asad Ali Malik

et al.

Applied Sciences, Journal Year: 2023, Volume and Issue: 13(4), P. 2411 - 2411

Published: Feb. 13, 2023

Smart cities are emerging rapidly due to the provisioning of comfort in human lifestyle. The healthcare system is an important segment smart city. timely delivery critical vital signs data emergency health centers without delay can save lives. Blockchain a secure technology that provides immutable record-keeping data. Secure transmission by avoiding erroneous also demands blockchain systems where patients’ history required for their necessary treatments. parameter each patient embedded separate block with SHA-256-based cryptography hash values. Mining computing nodes responsible find 32-bit nonce (number only used once) value compute valid technology. Computing values time-taking process may cause life losses system. Increasing mining reduces this delay; however, uniform distribution blocks these considering priority challenging task. In work, efficient scheme proposed scheduling tasks at ensure execution tasks. consists two parts, first one load balancing distribute among such makespan minimized and second part prioritizes more sensitive quick execution. results show effectively allocates different as compared round-robin greedy algorithms computes most higher-risk reduced amount time.

Language: Английский

Citations

17
Security of federated learning in 6G era: A review on conceptual techniques and software platforms used for research and analysis DOI
Syed Hussain Ali Kazmi, Faizan Qamar, Rosilah Hassan

et al.

Computer Networks, Journal Year: 2024, Volume and Issue: 245, P. 110358 - 110358

Published: March 30, 2024

Language: Английский

Citations

8
Meta-Fed IDS: Meta-Learning and Federated Learning Based Fog-Cloud Approach to Detect Known and Zero-Day Cyber Attacks in IoMT Networks DOI
Umer Zukaib, Xiaohui Cui, Chengliang Zheng

et al.

Journal of Parallel and Distributed Computing, Journal Year: 2024, Volume and Issue: 192, P. 104934 - 104934

Published: June 5, 2024

Language: Английский

Citations

6