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Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream

Intrusion Detection Systems (IDS) serve as critical components in safeguarding network security by detecting malicious activities. Although IDS has recently been treated primarily through the lens of machine learning, challenges persist, particularly with high-dimensional data and feature drift. Fea...

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Main Authors: Noori, Mustafa Sabah, Sahbudin, Ratna K. Z., Sali, Aduwati, Hashim, Fazirulhisyam
Format: Article
Published: Institute of Electrical and Electronics Engineers 2023
Online Access:http://psasir.upm.edu.my/id/eprint/107993/
https://ieeexplore.ieee.org/document/10318159/
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spelling my.upm.eprints.1079932024-09-26T04:36:14Z http://psasir.upm.edu.my/id/eprint/107993/ Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream Noori, Mustafa Sabah Sahbudin, Ratna K. Z. Sali, Aduwati Hashim, Fazirulhisyam Intrusion Detection Systems (IDS) serve as critical components in safeguarding network security by detecting malicious activities. Although IDS has recently been treated primarily through the lens of machine learning, challenges persist, particularly with high-dimensional data and feature drift. Feature drift pertains to the dynamic nature of feature significance, which can fluctuate over time, complicating the task of stable and effective intrusion detection. The existing Genetic Programming (GP)-combiner based ensemble classifier framework demonstrates notable efficiency in online intrusion detection, especially in accommodating concept drift. However, it does not adequately address the specific type of concept drift known as feature drift. To rectify this gap, this article proposes a refined version of GP-combiner, named Dynamic Feature Aware GP Ensemble (DFA-GPE). This advanced framework incorporates an improved variant of Variable Length Multi-Objective Particle Swarm Optimization (VLMO-PSO) to dynamically manage feature drift. The proposed VLMO-PSO employs a smart population initialization strategy based on Bernoulli distribution and symmetric uncertainty. It also utilizes a unique set of transfer functions that map the mobility equation outcomes to the decision space. To further optimize the process, the framework introduces a novel exemplar selection method, striking a balance between exploration and exploitation. DFA-GPE’s final feature selection decisions are informed by statistical analyses of feature weights, effectively addressing the challenge of dynamic feature selection as a multi-objective optimization problem that simultaneously enhances accuracy and conserves memory. Comprehensive evaluation of DFA-GPE on two benchmark datasets, namely HIKARI 2021 and TON_IoT 2020, reveals its robust performance across all metrics. From experiment results, our framework attains 99.09% and 92.64% accuracy on both datasets, respectively, while simultaneously reducing memory consumption. Hence, DFA-GPE emerges as a comprehensive framework adept at tackling the most pertinent issues related to stream data classification within IDS, notably outperforming existing methodologies. Institute of Electrical and Electronics Engineers 2023 Article PeerReviewed Noori, Mustafa Sabah and Sahbudin, Ratna K. Z. and Sali, Aduwati and Hashim, Fazirulhisyam (2023) Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream. IEEE Access, 11. pp. 128596-128617. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10318159/ 10.1109/access.2023.3333000
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Intrusion Detection Systems (IDS) serve as critical components in safeguarding network security by detecting malicious activities. Although IDS has recently been treated primarily through the lens of machine learning, challenges persist, particularly with high-dimensional data and feature drift. Feature drift pertains to the dynamic nature of feature significance, which can fluctuate over time, complicating the task of stable and effective intrusion detection. The existing Genetic Programming (GP)-combiner based ensemble classifier framework demonstrates notable efficiency in online intrusion detection, especially in accommodating concept drift. However, it does not adequately address the specific type of concept drift known as feature drift. To rectify this gap, this article proposes a refined version of GP-combiner, named Dynamic Feature Aware GP Ensemble (DFA-GPE). This advanced framework incorporates an improved variant of Variable Length Multi-Objective Particle Swarm Optimization (VLMO-PSO) to dynamically manage feature drift. The proposed VLMO-PSO employs a smart population initialization strategy based on Bernoulli distribution and symmetric uncertainty. It also utilizes a unique set of transfer functions that map the mobility equation outcomes to the decision space. To further optimize the process, the framework introduces a novel exemplar selection method, striking a balance between exploration and exploitation. DFA-GPE’s final feature selection decisions are informed by statistical analyses of feature weights, effectively addressing the challenge of dynamic feature selection as a multi-objective optimization problem that simultaneously enhances accuracy and conserves memory. Comprehensive evaluation of DFA-GPE on two benchmark datasets, namely HIKARI 2021 and TON_IoT 2020, reveals its robust performance across all metrics. From experiment results, our framework attains 99.09% and 92.64% accuracy on both datasets, respectively, while simultaneously reducing memory consumption. Hence, DFA-GPE emerges as a comprehensive framework adept at tackling the most pertinent issues related to stream data classification within IDS, notably outperforming existing methodologies.
format Article
author Noori, Mustafa Sabah
Sahbudin, Ratna K. Z.
Sali, Aduwati
Hashim, Fazirulhisyam
spellingShingle Noori, Mustafa Sabah
Sahbudin, Ratna K. Z.
Sali, Aduwati
Hashim, Fazirulhisyam
Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream
author_facet Noori, Mustafa Sabah
Sahbudin, Ratna K. Z.
Sali, Aduwati
Hashim, Fazirulhisyam
author_sort Noori, Mustafa Sabah
title Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream
title_short Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream
title_full Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream
title_fullStr Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream
title_full_unstemmed Feature drift aware for Intrusion Detection System using developed Variable Length Particle Swarm Optimization in data stream
title_sort feature drift aware for intrusion detection system using developed variable length particle swarm optimization in data stream
publisher Institute of Electrical and Electronics Engineers
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/107993/
https://ieeexplore.ieee.org/document/10318159/
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score 13.211869

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