Adversarial examples are inputs to a machine learning system intentionally crafted by an
attacker to fool the model into producing an incorrect output. These examples have achieved …

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 …

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 …

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

Intrusion Detection and the ability to detect attacks is a crucial aspect to ensure
cybersecurity. However, what if an IDS (Intrusion Detection System) itself is attacked; in other …

The incremental diffusion of machine learning algorithms in supporting cybersecurity is
creating novel defensive opportunities but also new types of risks. Multiple researches have …

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) …

The success of machine learning, particularly in supervised settings, has led to numerous
attempts to apply it in adversarial settings such as spam and malware detection. The core …

Deep learning begins to be widely applied in security applications, but the vulnerability of
deep learning in front of adversarial examples raises people's concern. In this paper, we …

Signature-based Network Intrusion Detection Systems (NIDSs) have traditionally been used
to detect malicious traffic, but they are incapable of detecting new threats. As a result …