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Publication
An intrusion detection system dataset for a multi-agent cyber-physical conveyor system
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Advisor(s)
Abstract(s)
Industry 4.0 is built upon the foundation of connecting
devices and systems via Internet of Things (IoT) technologies,
with Cyber-Physical Systems (CPS) serving as the backbone
infrastructure. Although this approach brings numerous benefits
like improved performance, responsiveness and reconfigurability,
it also introduces security concerns, making devices and systems
vulnerable to cyber attacks. There is a need for effective techniques
to protect these systems, and the availability of datasets
becomes essential to support the development of such techniques.
This paper presents a dataset based on the collection of traffic
information exchanged in a self-organizing conveyor system
using the multi-agent systems (MAS) architecture and containing
various intelligent conveyor modules. The dataset comprises
data collected at the network and agent levels under normal
system operation, denial of service (DoS) attacks, and malicious
agent attacks. An intrusion detection system that integrates Fast
Fourier Transform (FFT) and Machine Learning (ML) analysis
is developed to demonstrate the utility of this dataset.
Description
Keywords
Cyber-security Cyber-physical system Denial of Service Dataset Machine Learning
Citation
Funchal, Gustavo Silva; Zahid, Farzana; Melo, Victoria; Kuo, Matthew M.Y.; Pedrosa, Tiago; Sinha, Roopak; De la Prieta, Fernando; Leitão, Paulo (2023). An intrusion detection system dataset for a multi-agent cyber-physical conveyor system. In 2023 IEEE International Conference on Industrial Technology (ICIT). 04-06 April 2023, Orlando. ISSN 2643-2978. p. 1-6
Publisher
IEEE