Labeled data sets are necessary to train and evaluate anomaly-based network intrusion
detection systems. This work provides a focused literature survey of data sets for network …

Flow monitoring has become a prevalent method for monitoring traffic in high-speed
networks. By focusing on the analysis of flows, rather than individual packets, it is often said …

With the widespread use of encrypted data transport, network traffic encryption is becoming
a standard nowadays. This presents a challenge for traffic measurement, especially for …

Flow-based intrusion detection is an innovative way of detecting intrusions in high-speed
networks. Flow-based intrusion detection only inspects the packet header and does not …

This article describes an experimental investigation into the inter-dataset generalization of
supervised machine learning methods, trained to distinguish between benign and several …

Since network attacks become more sophisticated, it is difficult to discover them using
traditional analysis tools. For some kinds of attacks, it is necessary to analyze Application …

Network traffic anomaly detection is of critical importance in cybersecurity due to the massive
and rapid growth of sophisticated computer network attacks. Indeed, the more new Internet …

Flow-based approaches for SSH intrusion detection have been developed to overcome the
scalability issues of host-based alternatives. Although the detection of many SSH attacks in …

The work develops a network threat detection system, AI@ NTDS, that uses the behavioral
features of attackers and intelligent techniques. The proposed AI@ NTDS system combines …

Every day millions of people and hundreds of thousands of institutions communicate with
each other over the Internet. In the past two decades, while the number of people using the …