Adversarial attacks to machine learning-based smart healthcare systems
AKM Iqtidar Newaz, N Imtiazul Haque… - arXiv e …, 2020 - ui.adsabs.harvard.edu
The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and realtime monitoring to provide improved treatments to the …
diagnosis, progression, and realtime monitoring to provide improved treatments to the …
Adversarial attacks to machine learning-based smart healthcare systems
The increasing availability of healthcare data requires accurate analysis of disease
diagnosis, progression, and real-time monitoring to provide improved treatments to the …
diagnosis, progression, and real-time monitoring to provide improved treatments to the …
Recurring Threats to Smart Healthcare Systems Based on Machine Learning
Accurate analysis of illness diagnosis, progression, and real-time monitoring, made possible
by the ever-increasing availability of healthcare data, is essential for providing better …
by the ever-increasing availability of healthcare data, is essential for providing better …
Addressing adversarial machine learning attacks in smart healthcare perspectives
A Selvakkumar, S Pal, Z Jadidi - Sensing Technology: Proceedings of ICST …, 2022 - Springer
Smart healthcare systems are gaining popularity with the rapid development of intelligent
sensors, the Internet of Things (IoT) applications and services, and wireless …
sensors, the Internet of Things (IoT) applications and services, and wireless …
[PDF][PDF] Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems.
MH Abidi, H Alkhalefah… - CMES-Computer Modeling …, 2024 - researchgate.net
The healthcare data requires accurate disease detection analysis, real-time monitoring, and
advancements to ensure proper treatment for patients. Consequently, Machine Learning …
advancements to ensure proper treatment for patients. Consequently, Machine Learning …
Adversarial attacks against medical deep learning systems
The discovery of adversarial examples has raised concerns about the practical deployment
of deep learning systems. In this paper, we demonstrate that adversarial examples are …
of deep learning systems. In this paper, we demonstrate that adversarial examples are …
On the assessment of robustness of telemedicine applications against adversarial machine learning attacks
Telemedicine applications have been recently evolved to allow patients in underdeveloped
areas to receive medical services. Meanwhile, machine learning (ML) techniques have been …
areas to receive medical services. Meanwhile, machine learning (ML) techniques have been …
Systematically Assessing the Security Risks of AI/ML-enabled Connected Healthcare Systems
The adoption of machine-learning-enabled systems in the healthcare domain is on the rise.
While the use of ML in healthcare has several benefits, it also expands the threat surface of …
While the use of ML in healthcare has several benefits, it also expands the threat surface of …
An adversarial attack detection paradigm with swarm optimization
H Larijani, N Mtetwa, M Yousefi… - 2020 International Joint …, 2020 - ieeexplore.ieee.org
The rise of smart devices and applications has increased the dependence of human beings
on machine learning (ML) based code-driven systems. While many of the pragmatic …
on machine learning (ML) based code-driven systems. While many of the pragmatic …
An introduction to adversarial machine learning
Abstract Machine learning based system are increasingly being used for sensitive tasks
such as security surveillance, guiding autonomous vehicle, taking investment decisions …
such as security surveillance, guiding autonomous vehicle, taking investment decisions …