Attack Detection in IoT Critical Infrastructures: A Machine Learning and Big Data Processing Approach

The paper presents an approach to detection of attacks against Internet-of-Things networks and devices which can be used in critical infrastructures. It is based on use of machine learning and big data processing. Feature of the offered approach is using the method of reduction of output data sets a...

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Bibliographic Details
Published in2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) pp. 340 - 347
Main Authors Kotenko, Igor, Saenko, Igor, Kushnerevich, Alexey, Branitskiy, Alexander
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.02.2019
Subjects
attack detection
Big Data
critical infrastructure
distributed computing
Internet-of-Things
Machine learning
Security
Sparks
Task analysis
Training
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Summary:The paper presents an approach to detection of attacks against Internet-of-Things networks and devices which can be used in critical infrastructures. It is based on use of machine learning and big data processing. Feature of the offered approach is using the method of reduction of output data sets and application of various algorithms of machine learning based on distributed data processing. The paper compares the speed and accuracy of attack detection on the basis of machine learning algorithms in the local and distributed modes.
ISSN:2377-5750
DOI:10.1109/EMPDP.2019.8671571