Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware …
AA Bouramdane - Journal of Cybersecurity and Privacy, 2023 - mdpi.com
Smart grids have emerged as a transformative technology in the power sector, enabling efficient energy management. However, the increased reliance on digital technologies also …
L Yi, M Yin, M Darbandi - Transactions on Emerging …, 2023 - Wiley Online Library
Fog computing has arisen to complement cloud computing, offering a cost‐effective architecture to power the Internet of things. Fog computing is a network computing and …
Internet of Things (IoT) technologies serve as a backbone of cutting-edge intelligent systems. Machine Learning (ML) paradigms have been adopted within IoT environments to …
Intrusion detection systems (IDS) are a very vital part of network security, as they can be used to protect the network from illegal intrusions and communications. To detect malicious …
K Barik, S Misra, L Fernandez-Sanz - International Journal of Information …, 2024 - Springer
Abstract Artificial Intelligence (AI)-based IDS systems are susceptible to adversarial attacks and face challenges such as complex evaluation methods, elevated false positive rates …
M Pujari, BP Cherukuri, AY Javaid… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
Machine Learning (ML) techniques have been applied over the past two decades to improve the abilities of Intrusion Detection Systems (IDSs). Over time, several enhancements have …
U Sabeel, SS Heydari, K El-Khatib… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Agile network security is paramount in our modern world which is currently dominated by Internet systems and expanding digital spaces. This rapid digital transformation has created …
The rapid advancement of artificial intelligence within the realm of cybersecurity raises significant security concerns. The vulnerability of deep learning models in adversarial …