output
20142025
most citedMachine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations

162 citations

11 papers

cs.CV2025

Adaptive Image Restoration for Video Surveillance: A Real-Time Approach

Muhammad Awais Amin, Adama Ilboudo, Abdul Samad bin Shahid +2

One of the major challenges in the field of computer vision especially for detection, segmentation, recognition, monitoring, and automated solutions, is the quality of images. Imag…

cs.CR2022★ 162 cited

Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations

Muhammad Azmi Umer, Khurum Nazir Junejo, Muhammad Taha Jilani +1

Methods from machine learning are being applied to design Industrial Control Systems resilient to cyber-attacks. Such methods focus on two major areas: the detection of intrusions…

cs.NI2021★ 7 cited

Structured Nonnegative Matrix Factorization for Traffic Flow Estimation of Large Cloud Networks

Syed Muhammad Atif, Nicolas Gillis, Sameer Qazi +1

Network traffic matrix estimation is an ill-posed linear inverse problem: it requires to estimate the unobservable origin destination traffic flows, X, given the observable link tr…

cs.LG2021★ 16 cited

Performance Analysis of Fractional Learning Algorithms

Abdul Wahab, Shujaat Khan, Imran Naseem +1

Fractional learning algorithms are trending in signal processing and adaptive filtering recently. However, it is unclear whether the proclaimed superiority over conventional algori…

astro-ph.CO2021★ 6 cited

Constraining deceleration, jerk and transition redshift using cosmic chronometers, Type Ia supernovae and ISW effect

Syed Faisal ur Rahman

In this study we present constraints on the deceleration (q) and jerk (j) parameters using the late time integrated Sachs-Wolfe effect, type Ia supernovae, and H(z) data . We first…

cs.CR2021

Attack Rules: An Adversarial Approach to Generate Attacks for Industrial Control Systems using Machine Learning

Muhammad Azmi Umer, Chuadhry Mujeeb Ahmed, Muhammad Taha Jilani +1

Adversarial learning is used to test the robustness of machine learning algorithms under attack and create attacks that deceive the anomaly detection methods in Industrial Control…