With the increase in the usage of the Internet, a large amount of information is exchanged
between different communicating devices. The data should be communicated securely …

This survey paper describes a literature review of deep learning (DL) methods for cyber
security applications. A short tutorial-style description of each DL method is provided …

The rapid increase in network traffic has recently led to the importance of flow-based
intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly …

This survey paper describes a focused literature survey of machine learning (ML) and data
mining (DM) methods for cyber analytics in support of intrusion detection. Short tutorial …

Nowadays, there is a huge and growing concern about security in information and
communication technology among the scientific community because any attack or anomaly …

Network anomaly detection is an important and dynamic research area. Many network
intrusion detection methods and systems (NIDS) have been proposed in the literature. In this …

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

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 …

The connectivity of devices through the internet plays a remarkable role in our daily lives.
Many network-based applications are utilized in different domains, eg, health care, smart …

Over the last five years there has been an increase in the frequency and diversity of network
attacks. This holds true, as more and more organizations admit compromises on a daily …