Security issues in SCADA networks

Igure, Vinay M.; Laughter, Sean A.; Williams, Ronald D.

doi:10.1016/j.cose.2006.03.001

Cited by 456 publications

(207 citation statements)

References 2 publications

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“…Lots of real NIDSs based on these techniques had a good performance in the past decades, such as Next-Generation Intrusion Detection Expert System (NGIDES) [5] and Event Monitoring Enabling Responses to Anomalous Live Disturbances (EMERALD) [6], which were both developed by Stanford Research Institute (SRI). However, an increasing number of undetected attacks have arisen in industrial control network as of late, there are three factors leading to the situation [7]- [9]:…”

Section: Introductionmentioning

confidence: 99%

A Novel RNN-GBRBM Based Feature Decoder for Anomaly Detection Technology in Industrial Control Network

Hua

Zhu

Ma³

et al. 2017

IEICE Trans. Inf. & Syst.

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SUMMARYAs advances in networking technology help to connect industrial control networks with the Internet, the threat from spammers, attackers and criminal enterprises has also grown accordingly. However, traditional Network Intrusion Detection System makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new attack is employed. In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. The method employ network packets and extract high-quality features from raw features which is selected manually. A modified RNN-RBM is trained using the normal traffic in order to learn feature patterns of the normal network behaviors. Then the test traffic is analyzed against the learned normal feature pattern by using osPCA to measure the extent to which the test traffic resembles the learned feature pattern. Moreover, we design a semi-supervised incremental updating algorithm in order to improve the performance of the model continuously. Experiments show that our method is more efficient in anomaly detection than other traditional approaches for industrial control network.

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Section: Introductionmentioning

confidence: 99%

A Novel RNN-GBRBM Based Feature Decoder for Anomaly Detection Technology in Industrial Control Network

Hua

Zhu

Ma³

et al. 2017

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Three main trends can be identified in modern installations: (i) increased interconnection, allowing communication with corporate networks, remote access from engineers and system administrators, and even communication with the Internet; (ii) the use of low cost Commercial Off-The-Shelf (COTS) devices, such as Windows computers; and (iii) the adoption of the TCP/IP protocol stack [87]. These changes have a deep impact on the security of these systems.…”

Section: Evolution and Vulnerabilitiesmentioning

confidence: 99%

“…In [87], the authors identify general threats faced by SCADA environments and provide a list of research challenges in the area. The lack of Intrusion Detection Systems (IDS) for SCADA is one of the challenges addressed [37,144].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the special-purpose embedded devices are being replaced with COTS devices and the proprietary communication technology with the TCP/IP stack. These changes have a deep impact in the security of SCADA networks [87].…”

Section: Scada Security 41 Introductionmentioning

confidence: 99%

“…Examples of such efforts are the Dutch SCADA Security Good Practices for the Drinking Water Sector [102], the Recommended Guidelines for Information Security Baseline Requirements for Process Control, Safety and Support ICT Systems [113] by the Norwegian Oil and Gas Associ-ation and the North American Electric Reliability Coorporation (NERC) reliability standards on critical infrastructure protection [112]. Although a common recommendation in these guidelines is the implementation of Intrusion Detection Systems (IDSs), research in SCADA specific IDS is still in its infancy [137,87].…”

Section: Introductionmentioning

confidence: 99%

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