A comprehensive survey of generative adversarial networks (GANs) in cybersecurity intrusion detection

A Dunmore, J Jang-Jaccard, F Sabrina, J Kwak - IEEE Access, 2023‏ - ieeexplore.ieee.org
Generative Adversarial Networks (GANs) have seen significant interest since their
introduction in 2014. While originally focused primarily on image-based tasks, their capacity …

In-depth feature selection for the statistical machine learning-based botnet detection in IoT networks

R Kalakoti, S Nõmm, H Bahsi - IEEE Access, 2022‏ - ieeexplore.ieee.org
Attackers compromise insecure IoT devices to expand their botnets in order to launch more
influential attacks against their victims. In various studies, machine learning has been used …

Real-time botnet detection on large network bandwidths using machine learning

J Velasco-Mata, V González-Castro, E Fidalgo… - Scientific Reports, 2023‏ - nature.com
Botnets are one of the most harmful cyberthreats, that can perform many types of
cyberattacks and cause billionaire losses to the global economy. Nowadays, vast amounts …

Unsupervised learning for feature selection: A proposed solution for botnet detection in 5g networks

M Lefoane, I Ghafir, S Kabir… - IEEE Transactions on …, 2022‏ - ieeexplore.ieee.org
The world has seen exponential growth in deploying Internet of Things (IoT) devices. In
recent years, connected IoT devices have surpassed the number of connected non-IoT …

Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPS

JJ Molina-Campoverde, N Rivera-Campoverde… - Sensors, 2024‏ - mdpi.com
This study introduces an innovative algorithm for classifying transportation modes. It
categorizes modes such as walking, biking, tram, bus, taxi, and private vehicles based on …

Machine learning for botnet detection: An optimized feature selection approach

M Lefoane, I Ghafir, S Kabir, IU Awan - Proceedings of the 5th …, 2021‏ - dl.acm.org
Technological advancements have been evolving for so long, particularly Internet of Things
(IoT) technology that has seen an increase in the number of connected devices surpass non …

PhiKitA: Phishing kit attacks dataset for phishing websites identification

F Castaño, EF Fernañdez, R Alaiz-Rodríguez… - IEEE …, 2023‏ - ieeexplore.ieee.org
Recent studies have shown that phishers are using phishing kits to deploy phishing attacks
faster, easier and more massive. Detecting phishing kits in deployed websites might help to …

[HTML][HTML] Internet of Things botnets: A survey on Artificial Intelligence based detection techniques

M Lefoane, I Ghafir, S Kabir, IU Awan - Journal of Network and Computer …, 2025‏ - Elsevier
Abstract The Internet of Things (IoT) is a game changer when it comes to digitization across
industries. The Fourth Industrial Revolution (4IR), brought about a paradigm shift indeed …

An evolutionary computation-based machine learning for network attack detection in big data traffic

Y Wang, H Zhang, Y Wei, H Wang, Y Peng, Z Bin… - Applied Soft …, 2023‏ - Elsevier
Big data scenarios are characterized by multiple devices, massive traffic, and high data
dimensionality. In the process of attack identification, the selection of features from massive …

EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids

T Berghout, M Benbouzid - Reliability Engineering & System Safety, 2022‏ - Elsevier
Reliability and security of power distribution and data traffic in smart grid (SG) are very
important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is …