Towards a Reliable Intrusion Detection Benchmark Dataset

The urgently growing number of security threats on Internet and intranet networks highly demands reliable security solutions. Among various options, Intrusion Detection (IDSs) and Intrusion Prevention Systems (IPSs) are used to defend network infrastructure by detecting and preventing attacks and malicious activities. The performance of a detection system is evaluated using benchmark datasets. There exist a number of datasets, such as DARPA98, KDD99, ISC2012, and ADFA13, that have been used by researchers to evaluate the performance of their intrusion detection and prevention approaches. However, not enough research has focused on the evaluation and assessment of the datasets themselves and there is no reliable dataset in this domain. In this paper, we present a comprehensive evaluation of the existing datasets using our proposed criteria, a design and evaluation framework for IDS and IPS datasets, and a dataset generation model to create a reliable IDS or IPS benchmark dataset.