Machine Learning (ML) models are susceptible to adversarial samples that appear as
normal samples but have some imperceptible noise added to them with the intention of …

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) …

Cyber-security is the practice of protecting computing systems and networks from digital
attacks, which are a rising concern in the Information Age. With the growing pace at which …

With the increasing sophistication of cyber threats, the integration of machine learning (ML)
techniques in network security has become imperative for detecting and mitigating evolving …

Machine learning has witnessed tremendous growth in its adoption and advancement in the
last decade. The evolution of machine learning from traditional algorithms to modern deep …

Abstract Network Intrusion Detection System (NIDS) is a key component in securing the
computer network from various cyber security threats and network attacks. However …

Machine learning (ML) systems have been increasingly applied in web security applications
such as spammer detection, malware detection and fraud detection. These applications …

Due to their massive success in various domains, deep learning techniques are increasingly
used to design network intrusion detection solutions that detect and mitigate unknown and …

Abstract Machine Learning (ML) can be incredibly valuable to automate anomaly detection
and cyber-attack classification, improving the way that Network Intrusion Detection (NID) is …

Network-based Intrusion Detection System (NIDS) forms the frontline defence against
network attacks that compromise the security of the data, systems, and networks. In recent …