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Intrusion Detection...
Intrusion Detection in Industrial Networks via Data Streaming
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- Butun, Ismail, 1981 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Almgren, Magnus, 1972 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Gulisano, Vincenzo Massimiliano, 1984 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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- Papatriantafilou, Marina, 1966 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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(creator_code:org_t)
- 2020-07-01
- 2020
- English.
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In: Industrial IoT: Challenges, Design Principles, Applications, and Security. - Cham : Springer International Publishing. ; , s. 213-238
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Abstract
Subject headings
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- Given the increasing threat surface of industrial networks due to distributed, Internet-of-Things (IoT) based system architectures, detecting intrusions in Industrial IoT (IIoT) systems is all the more important, due to the safety implications of potential threats. The continuously generated data in such systems form both a challenge but also a possibility: data volumes/rates are high and require processing and communication capacity but they contain information useful for system operation and for detection of unwanted situations. In this chapter we explain that stream processing (a.k.a. data streaming) is an emerging useful approach both for general applications and for intrusion detection in particular, especially since it can enable data analysis to be carried out in the continuum of edge-fog-cloud distributed architectures of industrial networks, thus reducing communication latency and gradually filtering and aggregating data volumes. We argue that usefulness stems also due to facilitating provisioning of agile responses, i.e. due to potentially smaller latency for intrusion detection and hence also improved possibilities for intrusion mitigation. In the chapter we outline architectural features of IIoT networks, potential threats and examples of state-of-the art intrusion detection methodologies. Moreover, we give an overview of how leveraging distributed and parallel execution of streaming applications in industrial setups can influence the possibilities of protecting these systems. In these contexts, we give examples using electricity networks (a.k.a. Smart Grid systems). We conclude that future industrial networks, especially their Intrusion Detection Systems (IDSs), should take advantage of data streaming concept by decoupling semantics from the deployment.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Engineering (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Annan data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Other Computer and Information Science (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Datorsystem (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Computer Systems (hsv//eng)
Keyword
- data streaming
- intrusion detection
- edge computing
Publication and Content Type
- kap (subject category)
- vet (subject category)
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