A comprehensive survey of cybersecurity threats, attacks, and effective countermeasures in Industrial Internet of things
The Industrial Internet of Things (IIoT) ecosystem faces increased risks and vulnerabilities
due to adopting Industry 4.0 standards. Integrating data from various places and converging …
due to adopting Industry 4.0 standards. Integrating data from various places and converging …
Comprehensive tissue deconvolution of cell-free DNA by deep learning for disease diagnosis and monitoring
Plasma cell-free DNA (cfDNA) is a noninvasive biomarker for cell death of all organs.
Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases …
Deciphering the tissue origin of cfDNA can reveal abnormal cell death because of diseases …
Machine Learning: Models, Challenges, and Research Directions
T Talaei Khoei, N Kaabouch - Future Internet, 2023 - mdpi.com
Machine learning techniques have emerged as a transformative force, revolutionizing
various application domains, particularly cybersecurity. The development of optimal …
various application domains, particularly cybersecurity. The development of optimal …
Electricity theft detection using deep reinforcement learning in smart power grids
AT El-Toukhy, MM Badr, MMEA Mahmoud… - IEEE …, 2023 - ieeexplore.ieee.org
In smart power grids, smart meters (SMs) are deployed at the end side of customers to report
fine-grained power consumption readings periodically to the utility for energy management …
fine-grained power consumption readings periodically to the utility for energy management …
Countering evasion attacks for smart grid reinforcement learning-based detectors
Fraudulent customers in smart power grids employ cyber-attacks by manipulating their smart
meters and reporting false consumption readings to reduce their bills. To combat these …
meters and reporting false consumption readings to reduce their bills. To combat these …
Semi-supervised anomaly detection of dissolved oxygen sensor in wastewater treatment plants
As the world progresses toward a digitally connected and sustainable future, the integration
of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) …
of semi-supervised anomaly detection in wastewater treatment processes (WWTPs) …
SecFedIDM-V1: A Secure Federated Intrusion Detection Model With Blockchain and Deep Bidirectional Long Short-Term Memory Network
Cloud computing is a technology for efficiently using computing infrastructures and a
business model for selling computing resources and services. However, intruders find such …
business model for selling computing resources and services. However, intruders find such …
Data reduction in big data: a survey of methods, challenges and future directions
Data reduction plays a pivotal role in managing and analyzing big data, which is
characterized by its volume, velocity, variety, veracity, value, variability, and visibility …
characterized by its volume, velocity, variety, veracity, value, variability, and visibility …
Intrusion detection using chaotic poor and rich optimization with deep learning model for smart city environment
Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid
development of smart cities. To develop a smart environment, enhancements to the …
development of smart cities. To develop a smart environment, enhancements to the …
An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
L Concetti, G Mazzuto, FE Ciarapica, M Bevilacqua - Applied Sciences, 2023 - mdpi.com
Anomaly detection plays a crucial role in preserving industrial plant health. Detecting and
identifying anomalies helps prevent any production system from damage and failure. In …
identifying anomalies helps prevent any production system from damage and failure. In …