Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

Ustun, Taha Selim; Hussain, S. M. Suhail; Ulutas, Ahsen; Önen, Ahmet; Roomi, Muhammad M.; Mashima, Daisuke

doi:10.3390/sym13050826

Cited by 48 publications

(16 citation statements)

References 30 publications

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“…It has been seen that transfer of distribution of energy powered by Information and Communication Technologies (ICT) is known as Smart Grid. Reference [14] developed an IDS Intrusion detection System for Smart Grids using IEC 61850's GOOSE messages Generic Object-Oriented Substation Event. Reference [15] framed the planned communication among hypervisor observing its vSDN and the origin of new flow possibly starting a Distributed Denial of Service (DDoS) attack through switches, as an active Bayesian game of intrusion detection.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Ganesan¹,

Xavier²

2023

Intelligent Automation &Amp; Soft Computing

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Typically, smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks. These vulnerabilities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems. Thus, for this purpose, Intrusion detection system (IDS) plays a pivotal part in offering a reliable and secured range of services in the smart grid framework. Several existing approaches are there to detect the intrusions in smart grid framework, however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources. So as to overcome these limitations, the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anomalies in the smart grid network. In the grid side data acquisition, the power transmitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines. In this approach, power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC (Unified Power Quality Controller) and thereby storing the data in cloud storage. The data from smart grid cloud storage and KDD99 are pre-processed and are optimized using Improved Aquila Swarm Optimization (IASO) to extract optimal features. The probabilistic Recurrent Neural Network (PRNN) classifier is then employed for the prediction and classification of intrusions. At last, the performance is estimated and the outcomes are projected in terms of grid voltage, grid current, Total Harmonic Distortion (THD), voltage sag/swell, accuracy, precision, recall, F-score, false acceptance rate (FAR), and detection rate of the classifier. The analysis is compared with existing techniques to validate the proposed model efficiency.

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Section: Related Workmentioning

confidence: 99%

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Ganesan¹,

Xavier²

2023

Intelligent Automation &Amp; Soft Computing

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“…Many researchers have applied both dynamic features and static features to monitor and identify abnormal GOOSE messages in SASs [1,3,4,28,29,31,32]. However, some of them failed to provide detailed statistical results, such as a false-positive rate (FPR) and false-negative rate (FNR) [28,29] while the others did not consider or failed to detect stealthy attacks [1,3,4,32]. Therefore, more features are required to improve the accuracy of detecting stealthy attacks.…”

Section: A Network Features Of Goosementioning

confidence: 99%

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

et al. 2022

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In recent decades, cyber security issues in IEC 61850-compliant substation automation systems (SASs) have become growing concerns. Many researchers have developed various strategies to detect malicious behaviours of SASs during the system operational stage, such as anomaly-based detection. However, most existing anomaly-based detection methods identify an abnormal behaviour by checking every single network packet without any association. These traditional methods cannot effectively detect "stealthy" attacks which modify legitimate messages slightly while imitating patterns of benign behaviours. In this paper, we present feature selection and extraction methods to generalise and summarise critical features when detecting insider attacks triggering from untrusted control devices within SASs. By applying a sliding window-based sequential classification mechanism, our detection method can detect anomalies across multiple devices without the need to learn datasets collected from all devices. Firstly, to generalise critical features and summarise systems' behaviours so that it is unnecessary to collect all datasets, we selected and extracted six critical network features from generic object-oriented substation events (GOOSE) messages and seven summarised physical features based on the general architecture of the primary plant of distribution substations. After that, to improve detection accuracy and reduce computational costs, we applied sliding window algorithms to divide datasets into different overlapped window-based snippets. Then we applied a sequential classification model based on Bidirectional Long Short-Term Memory networks to train and test those datasets. As a result, our method can detect insider attacks across multiple devices accurately with a false-negative rate of less than 1%.

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“…Advanced monitoring system based on SCADA is a critical infrastructure for renewable integrated power system operations [45] - [46]. SCADA is a well-established computer-based automate system used to gather realtime data from remote or local equipment such as programmable logic controller, remote terminal unit (RTU), intelligent electronic devices (IED) and human machine interface (HMI), etc.…”

Section: Artificial Intelligence In Scada Intrusion Detectionmentioning

confidence: 99%

“…SCADA is a well-established computer-based automate system used to gather realtime data from remote or local equipment such as programmable logic controller, remote terminal unit (RTU), intelligent electronic devices (IED) and human machine interface (HMI), etc. [45]- [48]. Figure 4 explains the block diagram of SCADA architecture connected from operation center, energy management and distribution systems (EMS and DMS) through control room to the substation.…”

Section: Artificial Intelligence In Scada Intrusion Detectionmentioning

confidence: 99%

Artificial Intelligence and its applications in Renewable Integrated Power Systems

E.R.¹,

Joy²,

Bansal³

et al. 2022

Preprint

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Electric power system is undergoing major changes, due to large-scale integration of renewable energy, complicated network structure and increased energy demand. Developing autonomous systems, intelligent and digital communication technologies are becoming more and more urgent to modernize, strengthen and stabilize the power grid. Among several innovative efforts, artificial intelligence (AI) is becoming more popular due to its human like thinking logic, advanced wisdom, reasoning, learning capability and knowledge representation. AI approaches have demonstrated precise, faster and scalable outcomes, thus can be ideal to handle complex non-linear power systems. The recent technological developments in AI and hybrid techniques, makes it possible for solving large-scale complex power systems problems like control, planning, scheduling, prediction etc. This article has briefly discussed seven major areas, where AI has been used easily to handle constraints such as power system stability assessments, power system forecasting studies, power quality problems, optimization of generation scheduling, 5G network data communications, SCADA intrusion detection and transformer fault identification.

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Machine Learning-Based Intrusion Detection for Achieving Cybersecurity in Smart Grids Using IEC 61850 GOOSE Messages

Cited by 48 publications

References 30 publications

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Anomaly Detection for Insider Attacks From Untrusted Intelligent Electronic Devices in Substation Automation Systems

Artificial Intelligence and its applications in Renewable Integrated Power Systems

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