As network techniques rapidly evolve, attacks are becoming increasingly sophisticated and
threatening. Network intrusion detection has been widely accepted as an effective method to …

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 …

In this paper, we present a survey of deep learning approaches for cyber security intrusion
detection, the datasets used, and a comparative study. Specifically, we provide a review of …

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 …

With exponential growth in the size of computer networks and developed applications, the
significant increasing of the potential damage that can be caused by launching attacks is …

The exponential growth of the Internet of Things (IoT) devices provides a large attack surface
for intruders to launch more destructive cyber-attacks. The intruder aimed to exhaust the …

Over the past decades, researchers have been proposing different Intrusion Detection
approaches to deal with the increasing number and complexity of threats for computer …

The results of botnet detection methods are usually presented without any comparison.
Although it is generally accepted that more comparisons with third-party methods may help …

Deep learning (DL) is gaining significant prevalence in every field of study due to its
domination in training large data sets. However, several applications are utilizing machine …

The urgently growing number of security threats on Internet and intranet networks highly
demands reliable security solutions. Among various options, Intrusion Detection (IDSs) and …