About: Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #90285 Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection
sameAs	Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection
http://eprints.org/ontology/hasAccepted	Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (PDF)
http://eprints.org/ontology/hasDocument	Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (PDF) Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (Other) Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (Other) Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (Other) Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (Other) Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (Other)
dc:hasVersion	Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection (PDF)
Title	Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021-12-21
Creator	Gustavo Pessin Ralf Luis de Moura Virginia N. L. Franqueira
status	peer reviewed published
Publisher	IEEE
abstract	Cyber security is a topic of increasing relevance in relation to industrial networks. The higher intensity and intelligent use of data pushed by smart technology (Industry 4.0) together with an augmented integration between the operational technology (production) and the information technology (business) parts of the network have considerably raised the level of vulnerabilities. On the other hand, many industrial facilities still use serial networks as underlying communication system, and they are notoriously limited from a cyber security perspective since protection mechanisms available for TCP/IP communication do not apply. Therefore, an attacker gaining access to a serial network can easily control the industrial components, potentially causing catastrophic incidents, jeopardizing assets and human lives. This study proposes a framework to act as an anomaly detection system (ADS) for industrial serial networks. It has three ingredients: an unsupervised K-means component to analyse message content, a knowledge-based expert system component to analyse message metadata, and a voting process to generate alerts for security incidents, anomalous states, and faults. The framework was evaluated using the Profibus-DP, a network simulator which implements a serial bus system. Results for the simulated traffic were promising: 99.90% for accuracy, 99,64% for precision, and 99.28% for F1-Score. They indicate feasibility of the framework applied to serial-based industrial networks.
Is Part Of	Proceedings: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence ICTAI 2021 https://kar.kent.ac.uk/id/repository
Subject	QA Mathematics (inc Computing science)
list of authors	https://kar.kent.ac.uk/id/eprint/90285#authors
presented at	The 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #90285 Towards Safer Industrial Serial Networks: An Expert System Framework for Anomaly Detection

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