Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack Detection Article Swipe
Bader Alwasel
,
Abdulaziz Aldribi
,
Mohammed Alreshoodi
,
Ibrahim S. Alsukayti
,
Mohammed Alsuhaibani
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13137774
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13137774
In the dynamic and ever-evolving realm of network security, the ability to accurately identify and classify portscan attacks both inside and outside networks is of paramount importance. This study delves into the underexplored potential of fusing graph theory with machine learning models to elevate their anomaly detection capabilities in the context of industrial Internet of things (IIoT) network data analysis. We employed a comprehensive experimental approach, encompassing data preprocessing, visualization, feature analysis, and machine learning model comparison, to assess the efficacy of graph theory representation in improving classification accuracy. More specifically, we converted network traffic data into a graph-based representation, where nodes represent devices and edges represent communication instances. We then incorporated these graph features into our machine learning models. Our findings reveal that incorporating graph theory into the analysis of network data results in a modest-yet-meaningful improvement in the performance of the tested machine learning models, including logistic regression, support vector machines, and K-means clustering. These results underscore the significance of graph theory representation in bolstering the discriminative capabilities of machine learning algorithms when applied to network data.
Related Topics
Concepts
Computer science
Machine learning
Artificial intelligence
Graph kernel
Graph
Feature learning
Cluster analysis
Support vector machine
Network security
Graph theory
Preprocessor
Discriminative model
Data mining
Data pre-processing
Theoretical computer science
Kernel method
Radial basis function kernel
Computer security
Mathematics
Combinatorics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13137774
- https://www.mdpi.com/2076-3417/13/13/7774/pdf?version=1688353411
- OA Status
- gold
- Cited By
- 11
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4382982133
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4382982133Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app13137774Digital Object Identifier
- Title
-
Leveraging Graph-Based Representations to Enhance Machine Learning Performance in IIoT Network Security and Attack DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-30Full publication date if available
- Authors
-
Bader Alwasel, Abdulaziz Aldribi, Mohammed Alreshoodi, Ibrahim S. Alsukayti, Mohammed AlsuhaibaniList of authors in order
- Landing page
-
https://doi.org/10.3390/app13137774Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/13/7774/pdf?version=1688353411Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2076-3417/13/13/7774/pdf?version=1688353411Direct OA link when available
- Concepts
-
Computer science, Machine learning, Artificial intelligence, Graph kernel, Graph, Feature learning, Cluster analysis, Support vector machine, Network security, Graph theory, Preprocessor, Discriminative model, Data mining, Data pre-processing, Theoretical computer science, Kernel method, Radial basis function kernel, Computer security, Mathematics, CombinatoricsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 5, 2024: 5, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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