Online eigenvector transformation reflecting concept drift for improving network intrusion detection

Park, Seongchul; Seo, Sanghyun; Jeong, Changhoon; Kim, Jun Tae

doi:10.1111/exsy.12477

Cited by 6 publications

(5 citation statements)

References 31 publications

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“…In the literature, many studies in the field of IDS have addressed concept drift in data stream, as highlighted in Table I. Some have addressed challenges posed by data imbalance and introduced methods for reducing the dimensionality, as referenced in [13], [22], [23], [24], [5], [25], and [26]. Similarly, other researchers utilize ML-based IDS by integrating various machine learning classifiers to enhance feature selection and mitigate the effects of high dimensionality data [27].…”

Section: Related Workmentioning

confidence: 99%

Feature Drift Aware for Intrusion Detection System Using Developed Variable Length Particle Swarm Optimization in Data Stream

Noori,

Sahbudin,

Sali

et al. 2023

IEEE Access

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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 multiobjective 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.

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Section: Related Workmentioning

confidence: 99%

Feature Drift Aware for Intrusion Detection System Using Developed Variable Length Particle Swarm Optimization in Data Stream

Noori,

Sahbudin,

Sali

et al. 2023

IEEE Access

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“…At this time, the old samples will no longer be suitable for the new traffic classification requirements and even reduce the model's prediction accuracy. Therefore, the machine learning model in ML-NIDS is usually trained by online learning [33].…”

Section: Threat Modelmentioning

confidence: 99%

VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems

Liu

Zhang

et al. 2022

Sci. China Inf. Sci.

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Machine learning-based network intrusion detection systems (ML-NIDS) are extensively used for network security against unknown attacks. Existing intrusion detection systems can effectively defend traditional network attacks, however, they face AI based threats. The current known AI attacks cannot balance the escape rate and attack effectiveness. In addition, the time cost of existing AI attacks is very high. In this paper, we propose a backdoor attack called VulnerGAN, which features high concealment, high aggressiveness, and high timeliness. The backdoor can make the specific attack traffic bypass the detection of ML-NIDS without affecting the performance of ML-NIDS in identifying other attack traffic. VulnerGAN uses generative adversarial networks (GAN) to calculate poisoning and adversarial samples based on machine learning model vulnerabilities. It can make traditional network attack traffic escape black-box online ML-NIDS. At the same time, model extraction and fuzzing test are used to enhance the convergence of VulnerGAN. Compared with the state-of-the-art algorithms, the VulnerGAN backdoor attack increases 33.28% in concealment, 18.48% in aggressiveness, and 46.32% in timeliness.

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“…It may be executed automatically with planned software. Broadly, intrusion either on computers or in its database can disrupt computer security policies, namely availability, confidentiality and integrity (Park et al, 2020). In general, the purpose of intrusion detection systems (IDSs) is to protect systems from a variety of attacks and maintain the security policies of cyber systems all the time.…”

Section: Introductionmentioning

confidence: 99%

Detecting impersonation episode using teaching learning‐based optimization and support vector machine techniques

Shukla

2023

Expert Systems

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Online eigenvector transformation reflecting concept drift for improving network intrusion detection

Cited by 6 publications

References 31 publications

Feature Drift Aware for Intrusion Detection System Using Developed Variable Length Particle Swarm Optimization in Data Stream

Feature Drift Aware for Intrusion Detection System Using Developed Variable Length Particle Swarm Optimization in Data Stream

VulnerGAN: a backdoor attack through vulnerability amplification against machine learning-based network intrusion detection systems

Detecting impersonation episode using teaching learning‐based optimization and support vector machine techniques

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