Ubiquitous Application Testing on Cloud

Khan, Asharul Islam; Al‐Badi, Ali H.

doi:10.1109/icscee.2018.8538412

Cited by 3 publications

(1 citation statement)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As it is widely understood, the design of low power circuits is a critical operational factor of Industry 4.0, where devices such as interconnected sensors, actuators, and digital-analog signal converters are actively integrated into the IoT as autonomous mechanisms of the production process [ 3 , 10 , 12 ]. In low power combinational circuits, the dynamic power supply can receive a signal transition either as a functional or glitch.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Jiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

A majority of modern IoT/IIoT digital systems rely on cryptographic implementations to provide satisfactory levels of security. Hardware attacks such as side-channel analysis attacks or fault injection attacks can significantly degrade and even eliminate the desired level of security of the infrastructure in question. One of the most dangerous attacks of this type is voltage glitch attacks (VGAs), which can change the intended behavior of a system. By effectively manipulating the voltage at a specific time, an error can be injected that can change the intentional conduct and bypass system security features or even extract confidential information such as encryption keys by analyzing incorrect outputs of the firmware. This study proposes an innovative VGAs detection system based on advanced machine learning. Specifically, an innovative semisupervised learning methodology is used that utilizes a hybrid combination of algorithms. Specifically, a heuristic clustering method is used based on a linear fragmentation of group classes. In contrast, the ELM methodology is used as an algorithm for retrieving hidden variables through convex optimization.

show abstract

Section: Introductionmentioning

confidence: 99%

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Jiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Progressive Web Application Assessment Using AHP

Khan

Al‐Badi

Al-Kindi

2019

Procedia Computer Science

View full text Add to dashboard Cite

Review on Ubiquitous Clouds and Personal Mobile Networks

J¹

2019

JSWS

View full text Add to dashboard Cite

The cloud that frame the future of the computing has become more popular nowadays due to its numerous attractive benefits such as self-servicing, resource-pooling, elasticity, improved-scalability, resilience , and operational and migration flexibility. This server-client model with the pay per usage facility is described by the NIST as the enabling, on-demand, convenient-network access, with shared pool of computing resources that are provided swiftly with the minimal efforts of management and interactions. The paper gives a brief review on the continuous cloud computing services, explaining its ubiquity, and then proceeds with the usefulness of the cloud services in the personal mobile network, that could enable an enhancement in the performance of the personal mobile network in the future.

show abstract

Ubiquitous Application Testing on Cloud

Cited by 3 publications

References 30 publications

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Machine Learning Methods to Detect Voltage Glitch Attacks on IoT/IIoT Infrastructures

Progressive Web Application Assessment Using AHP

Review on Ubiquitous Clouds and Personal Mobile Networks

Contact Info

Product

Resources

About