Aims: The Intelligence Medical Diagnosis System (IMDS) has been targeted by the cyber terrorists, who aim to destroy the Critical National Infrastructure (CNI). This paper is motivated by the most recent incidents happened worldwide and have resulted in the compromise of diagnosis results. This study was undertaken to show how the IMDS could be attacked and diagnosis results compromised and present a set of cyber defense strategies to prevent against such attacks.
Methods And Results: This study used the ECGs data from the PhysioNet/Computing in Cardiology (CinC) Challenge 2017. We fed the data into our IMDS and launched a series of ethical hacking, which is specifically tailored to target IMDS. We proposed a set of cyber security strategies to prevent such compromise. We tested the effectiveness of our cyber defense strategies using an experiment. The results showed that the strategies were effective in protecting the IMDS diagnosis results from being compromised.
Conclusions: This study provides novel insights into the protection of IMDS and concludes that our cyber defense strategies can protect IMDS from being compromised by Brute Force and SQL Injection attacks.
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http://dx.doi.org/10.1109/EMBC.2019.8857166 | DOI Listing |
Data Brief
February 2025
School of Engineering and Technology, University of New South Wales, Canberra, Australia.
This dataset is generated from real-time simulations conducted in MATLAB/Simscape, focusing on the impact of smart noise signals on battery energy storage systems (BESS). Using Deep Reinforcement Learning (DRL) agent known as Proximal Policy Optimization (PPO), noise signals in the form of subtle millivolt and milliampere variations are strategically created to represent realistic cases of False Data Injection Attacks (FDIA). These signals are designed to disrupt the State of Charge (SoC) and State of Health (SoH) estimation blocks within Unscented Kalman Filters (UKF).
View Article and Find Full Text PDFSci Rep
January 2025
Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia.
The Internet of Medical Things (IoMT) has revolutionized healthcare by bringing real-time monitoring and data-driven treatments. Nevertheless, the security of communication between IoMT devices and servers remains a huge problem because of the inherent sensitivity of the health data and susceptibility to cyber threats. Current security solutions, including simple password-based authentication and standard Public Key Infrastructure (PKI) approaches, typically do not achieve an appropriate balance between security and low computational overhead, resulting in the possibility of performance bottlenecks and increased vulnerability to attacks.
View Article and Find Full Text PDFClin Transl Med
January 2025
Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.
Background: Paediatric sarcomas, including rhabdomyosarcoma, Ewing sarcoma and osteosarcoma, represent a group of malignancies that significantly contribute to cancer-related morbidity and mortality in children and young adults. These cancers share common challenges, including high rates of metastasis, recurrence or treatment resistance, leading to a 5-year survival rate of approximately 20% for patients with advanced disease stages. Despite the critical need, therapeutic advancements have been limited over the past three decades.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Information Technology Management, Faculty of Management Technology and Information System, Port Said University, Port Said, 42526, Egypt.
The Internet of Things (IoTs) has revolutionized cities, enabling them to become smarter. IoTs play an important role in monitoring the traffic cameras, roads, smart farming, connected vehicles, air quality, water level, humidity, and carbon dioxide pollution levels in city buildings. One of the major challenges of smart cities is the cyber threat to sensitive data.
View Article and Find Full Text PDFNeural Netw
December 2024
The School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China. Electronic address:
To tackle high communication costs and privacy issues in Centralized Federated Learning (CFL), Decentralized Federated Learning (DFL) is an alternative. However, a significant discrepancy exists between local updates and the expected global update, known as client drift, which arises from inconsistency and heterogeneous data. Previous research in the DFL field has focused on local information during client updates, without considering global information, which fails to alleviate the client drift issue.
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