Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways

Ashraf, Suleman; Shawon, Mohammad Hasanuzzaman; Khalid, Haris M.; Muyeen, S. M.

doi:10.3390/s21196415

Cited by 48 publications

(19 citation statements)

References 42 publications

(31 reference statements)

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“…Millions of people may experience significant power outages if an attacker exploits cyber security weaknesses. This study addresses this problem by presenting an OPNET-based network model exposed to many DoS assaults, illustrating the cyber security features of IEC 61850-based digital substations [ 117 ].…”

Section: Ml- and Dl-based Ids In Sdnmentioning

confidence: 99%

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Ahmed

Ngadi

Sharif

et al. 2022

Sensors

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A revolution in network technology has been ushered in by software defined networking (SDN), which makes it possible to control the network from a central location and provides an overview of the network’s security. Despite this, SDN has a single point of failure that increases the risk of potential threats. Network intrusion detection systems (NIDS) prevent intrusions into a network and preserve the network’s integrity, availability, and confidentiality. Much work has been done on NIDS but there are still improvements needed in reducing false alarms and increasing threat detection accuracy. Recently advanced approaches such as deep learning (DL) and machine learning (ML) have been implemented in SDN-based NIDS to overcome the security issues within a network. In the first part of this survey paper, we offer an introduction to the NIDS theory, as well as recent research that has been conducted on the topic. After that, we conduct a thorough analysis of the most recent ML- and DL-based NIDS approaches to ensure reliable identification of potential security risks. Finally, we focus on the opportunities and difficulties that lie ahead for future research on SDN-based ML and DL for NIDS.

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Section: Ml- and Dl-based Ids In Sdnmentioning

confidence: 99%

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Ahmed

Ngadi

Sharif

et al. 2022

Sensors

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“…It is hierarchical, self-sufcient, and makes use of machine learning and regression algorithms to identify network-level intrusions and anomalies in sensor data. Subsets of IoMT are subjected to extensive testing, such as wireless body area networks and other related medical devices [47][48][49][50][51][52][53]. Trough simulations, this research demonstrates the potential for achieving high detection accuracy with little resource use.…”

Section: Related Workmentioning

confidence: 99%

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Abdulwahid

2022

Computational Intelligence and Neuroscience

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The huge number of network traffic data, the abundance of available network features, and the diversity of cyber-attack patterns mean that intrusion detection remains difficult even though many earlier efforts have succeeded in building the Internet of Healthcare Things (IoHT). The implementation of an effective algorithm to filter out most of the probable outliers of Round Trip Time (RTT) of packets recorded in the Internet environment is urgently required. Congestion and interference in networks can arise when numerous biosensors in an IoHT system all attempt to communicate at once. Internet of Health Things networks are susceptible to both intra- and internetwork interference. In this research, the Server-Side Includes (SSI) attack is a key issue because it allows for network compromise as part of Internal Attacks. Despite recent advancements, SSI detection remains difficult due to the vast amounts of network traffic data, the abundance of network features, and the diversity of cyber-attack patterns (DDoS, DoS, Satan, spoofing, etc.). With the help of sensors, physiological data may be collected and sent to distant servers, where they can be analyzed in real time by doctors to help them catch diseases in their earliest stages. This is made possible by the Internet of medical things (IoMT). Wireless data transfer, however, leaves it vulnerable to hackers, especially if the data being transferred are particularly private or sensitive. Security measures designed for devices with more storage space and processing power will not work on those with less. However, machine learning for intrusion detection can give a tailored security response to the needs of IoMT systems. For SSI detection, current methods are either inefficient because of the large number of packets that need to be caught and analyzed or unsuccessful because of outlier values in the RTTs obtained from the captured TCP packets. To the same end, “downstream detection” refers to the process of calculating the total length of all connections made after a certain point. As a means of improving the SSI detection algorithm’s throughput in a network environment, packet RTT outliers will be eliminated. Flow records are used as inputs by flow-based NIDS to determine whether or not a given flow is malicious. In order to detect middlebox-based attacks from two Medical Health IoT datasets, this paper proposes a unique architecture of explainable neural networks (XNN). The model’s accuracy in classifying attacks in dataset 1 of the IoHT is 99.7%t, besides achieving 99.4% accuracy in categorising attacks on IoHT dataset 2.

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“…Since both the power distribution as well as communication networks are natural components of the critical infrastructure, security of power systems employing communication networks for control purposes has been addressed in several papers. For instance, authors in [13] present an OPNET based network model that is subjected to various Denial of Service (DoS) attacks to demonstrate cyber security aspect of an IEC-61850 based digital substations. The attack scenarios exhibit significant increases in the system delay and the prevention of messages from being transmitted within an acceptable time frame leading to malfunction of the devices such as unresponsiveness of IEDs, which could eventually lead to catastrophic scenarios under different fault conditions.…”

Section: State Of the Artmentioning

confidence: 99%

Impact of cyber‐attack on coordinated voltage control in low voltage grids

Farooq

Kamal

Gui

et al. 2022

IET Renewable Power Gen

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Power grid is facing several challenges such as voltage violation, power losses, and power quality issues due to the high integration of renewable energy sources such as solar and wind. One of the several strategies to overcome the voltage violation problem is the provision of reactive power from wind and solar power plants. Local controller of these plants coordinates with the controller to generate the reactive power. The coordinated controller performs its function based on the information obtained from the whole grid via communication network. Communication network infrastructure has high responsibility to ensure secure and stable services in order to provide reliable voltage control in distribution grids (DGs). Unstable or insecure communication networks can lead to several problems in the power system, such as increased power losses and in worse case blackouts. Therefore, this paper analyses the impact of cyber-attacks on the voltage quality control supported by the reactive power generation of PV plants in DGs. Two cyber-attack scenarios are demonstrated via a voltage coordination scenario based on a real distribution grid in Northern Denmark. Simulations are done in a Simulink model of Thy Mors Energi Grid, Denmark. A cyber control block is implemented within the coordinated controller in order to mitigate the effect of cyber-attack. Experimental results show that it is possible to detect and mitigate a cyber-attack such as denial of service and integrity attack before sending a control signal thus contributing towards a secure, stable, and resilient power grid.

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Denial-of-Service Attack on IEC 61850-Based Substation Automation System: A Crucial Cyber Threat towards Smart Substation Pathways

Cited by 48 publications

References 42 publications

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction

Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models

Impact of cyber‐attack on coordinated voltage control in low voltage grids

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