Integration of simulated annealing into pigeon inspired optimizer algorithm for feature selection in network intrusion detection systems Article Swipe
Wanwei Huang
,
Haobin Tian
,
Sun’an Wang
,
Chaoqin Zhang
,
Xiaohui Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.2176
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.7717/peerj-cs.2176
In the context of the 5G network, the proliferation of access devices results in heightened network traffic and shifts in traffic patterns, and network intrusion detection faces greater challenges. A feature selection algorithm is proposed for network intrusion detection systems that uses an improved binary pigeon-inspired optimizer (SABPIO) algorithm to tackle the challenges posed by the high dimensionality and complexity of network traffic, resulting in complex models, reduced accuracy, and longer detection times. First, the raw dataset is pre-processed by uniquely one-hot encoded and standardized. Next, feature selection is performed using SABPIO, which employs simulated annealing and the population decay factor to identify the most relevant subset of features for subsequent review and evaluation. Finally, the selected subset of features is fed into decision trees and random forest classifiers to evaluate the effectiveness of SABPIO. The proposed algorithm has been validated through experimentation on three publicly available datasets: UNSW-NB15, NLS-KDD, and CIC-IDS-2017. The experimental findings demonstrate that SABPIO identifies the most indicative subset of features through rational computation. This method significantly abbreviates the system’s training duration, enhances detection rates, and compared to the use of all features, minimally reduces the training and testing times by factors of 3.2 and 0.3, respectively. Furthermore, it enhances the F1-score of the feature subset selected by CPIO and Boost algorithms when compared to CPIO and XGBoost, resulting in improvements ranging from 1.21% to 2.19%, and 1.79% to 4.52%.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.7717/peerj-cs.2176
- OA Status
- gold
- Cited By
- 2
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400693986
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400693986Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7717/peerj-cs.2176Digital Object Identifier
- Title
-
Integration of simulated annealing into pigeon inspired optimizer algorithm for feature selection in network intrusion detection systemsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-16Full publication date if available
- Authors
-
Wanwei Huang, Haobin Tian, Sun’an Wang, Chaoqin Zhang, Xiaohui ZhangList of authors in order
- Landing page
-
https://doi.org/10.7717/peerj-cs.2176Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.7717/peerj-cs.2176Direct OA link when available
- Concepts
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Feature selection, Computer science, Intrusion detection system, Simulated annealing, Curse of dimensionality, Random forest, Data mining, Ranging, Decision tree, Population, Artificial intelligence, Algorithm, Machine learning, Pattern recognition (psychology), Sociology, Telecommunications, DemographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2Per-year citation counts (last 5 years)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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