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 …

Abstract The Internet of Things (IoT) is one of the main research fields in the Cybersecurity
domain. This is due to (a) the increased dependency on automated device, and (b) the …

Anomaly based intrusion detection systems suffer from a lack of appropriate evaluation data
sets. Often, existing data sets may not be published due to privacy concerns or do not reflect …

Publicly available labelled data sets are necessary for evaluating anomaly-based Intrusion
Detection Systems (IDSs). However, existing data sets are often not up-to-date or not yet …

IP Addresses are a central part of packet-and flow-based network data. However,
visualization and similarity computation of IP Addresses are challenging to due the missing …

Frequently, port scans are early indicators of more serious attacks. Unfortunately, the
detection of slow port scans in company networks is challenging due to the massive amount …

Reproducibility of network intrusion detection research necessitates widely available
datasets that represent real-world scenarios. One of the key omissions of existing datasets …

Insider threat has become a recurring and serious challenge faced by organization
throughout the world. It shows an increasing trend from 2003 onwards. Due to the …

Network intrusion detection systems (NIDS) are critical to defending network systems from
cyber attacks. Recently, machine learning has been applied to enhance NIDS capability. To …

An insider, also regarded as an employee of a company, becomes a threat when the
intention or action can affect the company negatively. Insider threat has been an eminent …