An intrusion detection system (IDS) is critical in protecting organizations from cyber threats.
The susceptibility of Machine Learning and Deep Learning-based IDSs against adversarial …

Abstract The rise of Adversarial Machine Learning (AML) attacks is presenting a significant
challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address …

Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

Adversarial machine learning is a recent area of study that explores both adversarial attack
strategy and detection systems of adversarial attacks, which are inputs specially crafted to …

Abstract Intrusion Detection Systems (IDS) are essential tools to protect network security
from malicious traffic. IDS have recently made significant advancements in their detection …

In this research, we focus on investigating the effectiveness of different adversarial attacks
and robustness of deep learning-based Intrusion detection using different Neural networks …

Abstract Machine learning is one of the predominant methods used in computer science and
has been widely and successfully applied in many areas such as computer vision, pattern …

Intrusion detection systems are an essential part of any cybersecurity architecture. These
systems are critical in defending networks against a variety of security threats. In recent …

Security holds significant importance in our daily lives, as any compromise in confidentiality
can pose a serious threat from criminals. Cybercriminals constantly seek ways to exploit …

Abstract In recent years, Deep Neural Networks (DNN) have demonstrated remarkable
success in various domains, including Intrusion Detection Systems (IDS). The ability of DNN …