Enhanced Intrusion Detection with Data Stream Classification and Concept Drift Guided by the Incremental Learning Genetic Programming Combiner
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- @Article{shyaa:2023:Sensors,
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author = "Methaq A. Shyaa and Zurinahni Zainol and
Rosni Abdullah and Mohammed Anbar and Laith Alzubaidi and
Jose Santamaria",
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title = "Enhanced Intrusion Detection with Data Stream
Classification and Concept Drift Guided by the
Incremental Learning Genetic Programming Combiner",
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journal = "Sensors",
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year = "2023",
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volume = "23",
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number = "7",
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pages = "Article No. 3736",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1424-8220",
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URL = "https://www.mdpi.com/1424-8220/23/7/3736",
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DOI = "doi:10.3390/s23073736",
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abstract = "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 100percent and 98percent by
GPC-KOS and GPC-FOS, respectively. Additionally, our
GPC variants do not show superior performance in
handling blip drift.",
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notes = "also known as \cite{s23073736}",
- }
Genetic Programming entries for
Methaq A Shyaa
Zurinahni Zainol
Rosni Abdullah
Mohammed Anbar
Laith Alzubaidi
Jose Santamaria
Citations