Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner Article Swipe
Methaq A. Shyaa
,
Zurinahni Zainol
,
Rosni Abdullah
,
Mohammed Anbar
,
Laith Alzubaidi
,
José Santamaría
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23073736
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23073736
Concept drift (CD) in data streaming scenarios such as networking intrusion detection systems (IDS) refers to the change in the statistical distribution of the data over time. There are five principal variants related to CD: incremental, gradual, recurrent, sudden, and blip. Genetic programming combiner (GPC) classification is an effective core candidate for data stream classification for IDS. However, its basic structure relies on the usage of traditional static machine learning models that receive onetime training, limiting its ability to handle CD. To address this issue, we propose an extended variant of the GPC using three main components. First, we replace existing classifiers with alternatives: online sequential extreme learning machine (OSELM), feature adaptive OSELM (FA-OSELM), and knowledge preservation OSELM (KP-OSELM). Second, we add two new components to the GPC, specifically, a data balancing and a classifier update. Third, the coordination between the sub-models produces three novel variants of the GPC: GPC-KOS for KA-OSELM; GPC-FOS for FA-OSELM; and GPC-OS for OSELM. This article presents the first data stream-based classification framework that provides novel strategies for handling CD variants. The experimental results demonstrate that both GPC-KOS and GPC-FOS outperform the traditional GPC and other state-of-the-art methods, and the transfer learning and memory features contribute to the effective handling of most types of CD. Moreover, the application of our incremental variants on real-world datasets (KDD Cup ‘99, CICIDS-2017, CSE-CIC-IDS-2018, and ISCX ‘12) demonstrate improved performance (GPC-FOS in connection with CSE-CIC-IDS-2018 and CICIDS-2017; GPC-KOS in connection with ISCX2012 and KDD Cup ‘99), with maximum accuracy rates of 100% and 98% by GPC-KOS and GPC-FOS, respectively. Additionally, our GPC variants do not show superior performance in handling blip drift.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23073736
- https://www.mdpi.com/1424-8220/23/7/3736/pdf?version=1681175960
- OA Status
- gold
- Cited By
- 22
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362639444
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4362639444Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23073736Digital Object Identifier
- Title
-
Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming CombinerWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-04Full publication date if available
- Authors
-
Methaq A. Shyaa, Zurinahni Zainol, Rosni Abdullah, Mohammed Anbar, Laith Alzubaidi, José SantamaríaList of authors in order
- Landing page
-
https://doi.org/10.3390/s23073736Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/23/7/3736/pdf?version=1681175960Direct 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/1424-8220/23/7/3736/pdf?version=1681175960Direct OA link when available
- Concepts
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Concept drift, Computer science, Intrusion detection system, Artificial intelligence, Classifier (UML), Genetic programming, Machine learning, Data stream, Data mining, Limiting, Data stream mining, Pattern recognition (psychology), Engineering, Telecommunications, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
22Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 14, 2024: 6, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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