A Multi-Agent Adaptive Architecture for Smart-Grid-Intrusion Detection and Prevention

Kisielewicz, T.; Stanek, Stanisław; Żytniewski, Mariusz

doi:10.3390/en15134726

Cited by 7 publications

(4 citation statements)

References 33 publications

(42 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Data integrity attacks [13,21,25,29,40,41,48,49,53,55,[59][60][61][62]70,75,76] Unusual consumption behaviors and measurements [6,24,27,32,34,35,38,46,52,67,68,[71][72][73] Network intrusions [16,18,19,56,63,69] Network infrastructure anomalies [14,15,17,20,22,33,39,47,58,64] Electrical data anomalies [7,23,26,36,…”

Section: Study Object Papermentioning

confidence: 99%

See 1 more Smart Citation

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Guato Burgos,

Morato,

Vizcaino Imacaña

2024

Applied Sciences

View full text Add to dashboard Cite

The size of power grids and a complex technological infrastructure with higher levels of automation, connectivity, and remote access make it necessary to be able to detect anomalies of various kinds using optimal and intelligent methods. This paper is a review of studies related to the detection of anomalies in smart grids using AI. Digital repositories were explored considering publications between the years 2011 and 2023. Iterative searches were carried out to consider studies with different approaches, propose experiments, and help identify the most applied methods. Seven objects of study related to anomalies in SG were identified: attacks on data integrity, unusual measurements and consumptions, intrusions, network infrastructure, electrical data, identification of cyber-attacks, and use of detection devices. The issues relating to cybersecurity prove to be widely studied, especially to prevent intrusions, fraud, data falsification, and uncontrolled changes in the network model. There is a clear trend towards the conformation of anomaly detection frameworks or hybrid solutions. Machine learning, regression, decision trees, deep learning, support vector machines, and neural networks are widely used. Other proposals are presented in novel forms, such as federated learning, hyperdimensional computing, and graph-based methods. More solutions are needed that do not depend on a lot of data or knowledge of the network model. The use of AI to solve SG problems is generating an evolution towards what could be called next-generation smart grids. At the end of this document is a list of acronyms and terminology.

show abstract

Section: Study Object Papermentioning

confidence: 99%

“…Enhanced network user monitoring can effectively reduce the risk of data leakage, phishing, and spam attacks through the implementation of statistically based detection models and an adaptive multi-agent architecture [69]. An architecture, as such, will not indicate what a system can do, but how it should work.…”

Section: Network Intrusionsmentioning

confidence: 99%

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Guato Burgos,

Morato,

Vizcaino Imacaña

2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…It is possible to utilize an IPS to detect when workers or visitors to a network are breaking the regulations set out by the company. In contrast, an IDS is just tasked with monitoring the network and alerting system administrators to any suspicious activity [131].…”

Section: Ips and Idsmentioning

confidence: 99%

Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

et al. 2023

View full text Add to dashboard Cite

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.

show abstract

“…Modern smart grids [17][18][19] today include new controls, communications, smart monitoring, and self-healing products, technologies, and services. These organizations offer a variety of advantages, including simple connection and operating efficiency for generators of all sizes and technology [20] . Customers here are aware of the information about the systems and play a crucial part in enhancing operation of the system.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Muthubalaji,

Muniyaraj,

Rao

et al. 2024

Big Data Min. Anal.

View full text Add to dashboard Cite

Big data has the ability to open up innovative and ground-breaking prospects for the electrical grid, which also supports to obtain a variety of technological, social, and financial benefits. There is an unprecedented amount of heterogeneous big data as a consequence of the growth of power grid technologies, along with data processing and advanced tools. The main obstacles in turning the heterogeneous large dataset into useful results are computational burden and information security. The original contribution of this paper is to develop a new big data framework for detecting various intrusions from the smart grid systems with the use of AI mechanisms. Here, an AdaBelief Exponential Feature Selection (AEFS) technique is used to efficiently handle the input huge datasets from the smart grid for boosting security. Then, a Kernel based Extreme Neural Network (KENN) technique is used to anticipate security vulnerabilities more effectively. The Polar Bear Optimization (PBO) algorithm is used to efficiently determine the parameters for the estimate of radial basis function. Moreover, several types of smart grid network datasets are employed during analysis in order to examine the outcomes and efficiency of the proposed AdaBelief Exponential Feature Selection-Kernel based Extreme Neural Network (AEFS-KENN) big data security framework. The results reveal that the accuracy of proposed AEFS-KENN is increased up to 99.5% with precision and AUC of 99% for all smart grid big datasets used in this study.

show abstract

A Multi-Agent Adaptive Architecture for Smart-Grid-Intrusion Detection and Prevention

Cited by 7 publications

References 33 publications

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

A Review of Smart Grid Anomaly Detection Approaches Pertaining to Artificial Intelligence

Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Contact Info

Product

Resources

About