An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids

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aut.relation.endpage288
aut.relation.issue6
aut.relation.journalAlgorithms
aut.relation.startpage288
aut.relation.volume16
dc.contributor.authorLi, Xue Jun
dc.contributor.authorMa, Maode
dc.contributor.authorSun, Yihan
dc.date.accessioned2023-06-12T01:04:06Z
dc.date.available2023-06-12T01:04:06Z
dc.date.issued2023-06-02
dc.description.abstractModern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids.
dc.identifier.citationAlgorithms, ISSN: 1999-4893 (Print); 1999-4893 (Online), MDPI AG, 16(6), 288-288. doi: 10.3390/a16060288
dc.identifier.doi10.3390/a16060288
dc.identifier.issn1999-4893
dc.identifier.issn1999-4893
dc.identifier.urihttps://hdl.handle.net/10292/16243
dc.languageen
dc.publisherMDPI AG
dc.relation.urihttps://www.mdpi.com/1999-4893/16/6/288
dc.rights.accessrightsOpenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.subject4604 Cybersecurity and Privacy
dc.subject4605 Data Management and Data Science
dc.subject4611 Machine Learning
dc.subject01 Mathematical Sciences
dc.subject08 Information and Computing Sciences
dc.subject09 Engineering
dc.subject40 Engineering
dc.subject46 Information and computing sciences
dc.subject49 Mathematical sciences
dc.titleAn Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids
dc.typeJournal Article
pubs.elements-id509015
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