A resource-preserving self-regulating Uncoupled MAC algorithm to be applied in incident detection

A resource-preserving self-regulating Uncoupled MAC algorithm to be applied in incident detection

The connectivity of embedded systems is increasing accompanied with thriving technology such as Internet of Things/Everything (IoT/E), Connected Cars, Smart Cities, Industry 4.0, 5G or Software-Defined Everything. Apart from the benefits of these trends, the continuous networking offers hackers a broad spectrum of attack vectors. The identification of attacks or unknown behavior through Intrusion Detection Systems (IDS) has established itself as a conducive and mandatory mechanism apart from the protection by cryptographic schemes in a holistic security eco-system. In systems where resources are valuable goods and stand in contrast to the ever increasing amount of network traffic, sampling has become a useful utility in order to detect malicious activities on a manageable amount of data. In this work an algorithm – Uncoupled MAC – is presented which secures network communication through a cryptographic scheme by uncoupled Message Authentication Codes (MAC) but as a side effect also provides IDS functionality producing alarms based on the violation of Uncoupled MAC values. Through a novel self-regulation extension, the algorithm adapts it’s sampling parameters based on the detection of malicious actions. The evaluation in a virtualized environment clearly shows that the detection rate increases over runtime for different attack scenarios. Those even cover scenarios in which intelligent attackers try to exploit the downsides of sampling.

This is a preprint version of the article.

Keywords: network security, adaptive intrusion detection, message authentication, self-regulation, resource conservation

Year: 2019

Journal ISSN: 0167-4048
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Authors of this publication:

Michael Heigl

E-mail: heigl@kiv.zcu.cz

Michael is currently working as a research associate at the institute ProtectIT at the Deggendorf Institute of Technology and holds a Ph.D. degree from the University of West Bohemia for his dissertation on machine learning enhanced network-based anomaly detection. He is specialized in improving outlier detection methods for streaming data applications.

Dalibor Fiala

Phone: +420 377 63 2429
E-mail: dalfia@kiv.zcu.cz
WWW: http://www.kiv.zcu.cz/~dalfia/

Dalibor is the research group coordinator and an associate professor at the Department of Computer Science and Engineering at the University of West Bohemia in Pilsen, Czech Republic. He is interested in data mining, web mining, information retrieval, informetrics, and information science.

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Data Mining for Computer Networks Security

Authors:  Michael Heigl, Laurin Doerr, Dalibor Fiala
Desc.:Novel data mining methods for the enhancement of computer networks security using advanced outlier detection techniques on streaming data are investigated.