Recently, deep learning has been successfully applied to network security assessments and
intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional …

Neural networks are increasingly important in the development of Network Intrusion
Detection Systems (NIDS), as they have the potential to achieve high detection accuracy …

Adversarial attacks pose a major threat to machine learning and to the systems that rely on
it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading …

Deep neural networks (DNNs) are commonly used for various traffic analysis problems, such
as website fingerprinting and flow correlation, as they outperform traditional (eg, statistical) …

Network security has become a serious issue since networks are vulnerable and subject to
increasing intrusive activities. Therefore, network intrusion detection systems (IDSs) are an …

Detecting various types of attack traffic is critical to computer network security. The current
detection methods require massive amounts of data to detect attack traffic. However, in most …

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 …

With the rapid development and widespread application of cloud computing, cloud
computing open networks and service sharing scenarios have become more complex and …

Traffic classification is the first step in network anomaly detection and is essential to network
security. However, existing malicious traffic classification methods have several limitations; …

The surge in the internet of things (IoT) devices seriously threatens the current IoT security
landscape, which requires a robust network intrusion detection system (NIDS). Despite …