Toward Secure Microfluidic Fully Programmable Valve Array Biochips (original) (raw)

Indonesian Journal of Electrical Engineering and Computer Science

2021

Received Jul 8, 2021 Revised Oct 19, 2021 Accepted Oct 26, 2021 The Coronavirus disease (COVID-19) pandemic is the most recent threat to global health. Reverse transcription-polymerase chain reaction (RT-PCR) testing, computed tomography (CT) scans, and chest X-ray (CXR) images are being used to identify Coronavirus, one of the most serious community viruses of the twenty-first century. Because CT scans and RT-PCR analyses are not available in most health divisions, CXR images are typically the most time-saving and cost-effective tool for physicians in making decisions. Artificial intelligence and machine learning have become increasingly popular because of recent technical advancements. The goal of this project is to combine machine learning, deep learning, and the health-care sector to create a categorization technique for detecting the Coronavirus and other respiratory disorders. The three conditions evaluated in this study were COVID-19, viral Pneumonia, and normal lungs. Using ...

Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems

Sensors

Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicat...

Machine-Learning Classifiers for Security in Connected Medical Devices

2017 26th International Conference on Computer Communication and Networks (ICCCN), 2017

Medical devices equipped with wireless connectivity and remote monitoring features are increasingly becoming connected to each other, to an outside programmer and even to the Internet. While Internet of Things technology enables health-care professionals to fine tune or modify medical device settings without invasive procedures, this also opens up large attack surfaces and introduces potential security vulnerabilities. Medical device hacks are slowly becoming a reality and it becomes more critical than ever to defend and protect these devices from security attacks. In this paper, we assess the feasibility of using machine learning models to efficiently determine attacks targeted on a medical device. Specifically, we develop feature sets to accurately profile a medical device and observe any deviation from its normal behavior. We test our method using different machine learning algorithms and provide a comparison analysis of the detection results.