Industrial Control System Traffic Data Sets for Intrusion Detection Research - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2014

Industrial Control System Traffic Data Sets for Intrusion Detection Research


Supervisory control and data acquisition (SCADA) systems monitor and control physical processes associated with the critical infrastructure. Weaknesses in the application layer protocols, however, leave SCADA networks vulnerable to attack. In response, cyber security researchers have developed myriad intrusion detection systems. Researchers primarily rely on unique threat models and the corresponding network traffic data sets to train and validate their intrusion detection systems. This leads to a situation in which researchers cannot independently verify the results, cannot compare the effectiveness of different intrusion detection systems, and cannot adequately validate the ability of intrusion detection systems to detect various classes of attacks. Indeed, a common data set is needed that can be used by researchers to compare intrusion detection approaches and implementations. This paper describes four data sets, which include network traffic, process control and process measurement features from a set of 28 attacks against two laboratory-scale industrial control systems that use the MODBUS application layer protocol. The data sets, which are freely available, enable effective comparisons of intrusion detection solutions for SCADA systems.
Fichier principal
Vignette du fichier
978-3-662-45355-1_5_Chapter.pdf (1.5 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01386754 , version 1 (24-10-2016)





Thomas Morris, Wei Gao. Industrial Control System Traffic Data Sets for Intrusion Detection Research. 8th International Conference on Critical Infrastructure Protection (ICCIP), Mar 2014, Arlington, United States. pp.65-78, ⟨10.1007/978-3-662-45355-1_5⟩. ⟨hal-01386754⟩
322 View
1245 Download



Gmail Facebook X LinkedIn More