article · Neural Networks
Industrial operations rely on Supervisory Control and Data Acquisition architectures to monitor and automate machinery over network connections. Because these systems are frequently connected to the internet without built-in security frameworks, they face considerable risks from cyber-attacks. An intrusion detection method combining the Genetically Seeded Flora feature optimisation algorithm with a Transformer Neural Network addresses these vulnerabilities. Instead of relying on traditional signature-based detection methods, this approach identifies deviations and shifts in operational patterns that indicate unauthorised interference. Experimental evaluations using the benchmark WUSTL-IIOT-2018 industrial control systems dataset demonstrate the capability of the technique. The combined model outperforms standard deep learning architectures, including Residual Neural Networks, Recurrent Neural Networks, and Long Short-Term Memory networks, delivering superior accuracy and computational efficiency in identifying cyber intrusions within industrial control networks.
Industrial machinery and infrastructure depend heavily on supervisory networks to function continuously. When these networks are breached, the physical operation of essential services can be disrupted. Employing pattern-based deep learning helps protect vital industrial systems from novel cyber threats that traditional signature-based security tools frequently fail to recognise, strengthening resilience against attacks.
This method could provide network security monitoring software for operators of industrial control and automation machinery. The technology appears to be applied and tested in an experimental research setting, having demonstrated performance on a standard industrial control dataset rather than in live operational environments. Industrial cybersecurity providers seeking to replace or augment signature-based network intrusion detection tools could explore the model to improve anomaly detection efficiency.
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Supervisory Control and Data Acquisition (SCADA) systems are computer-based control architectures specifically engineered for the operation of industrial machinery via hardware and software models. These systems are used to project, monitor, and automate the state of the operational network through the utilization of ethernet links, which enable two-way communications. However, as a result of their constant connectivity to the internet and the lack of security frameworks within their internal architecture, they are susceptible to cyber-attacks. In light of this, we have proposed an intrusion detection algorithm, intending to alleviate this security bottleneck. The proposed algorithm, the Genetically Seeded Flora (GSF) feature optimization algorithm, is integrated with Transformer Neural Network (TNN) and functions by detecting changes in operational patterns that may be indicative of an intruder's involvement. The proposed Genetically Seeded Flora Transformer Neural Network (GSFTNN) algorithm stands in stark contrast to the signature-based method employed by traditional intrusion detection systems. To evaluate the performance of the proposed algorithm, extensive experiments are conducted using the WUSTL-IIOT-2018 ICS SCADA cyber security dataset. The results of these experiments indicate that the proposed algorithm outperforms traditional algorithms such as Residual Neural Networks (ResNet), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM) in terms of accuracy and efficiency.
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DOI: 10.1016/j.neunet.2023.05.047
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