A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification

Ding, Hongwei; Cui, Xiaohui

doi:10.1007/s12652-023-04610-z

J Ambient Intell Human Comput

2023

DOI: 10.1007/s12652-023-04610-z

|View full text |Cite

A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification

Hongwei Ding

Xiaohui Cui

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Leveraging Gametic Heredity in Oversampling Techniques to Handle Class Imbalance for Efficient Cyberthreat Detection in IIoT

Verma,

Breslin,

O’Shea

et al. 2024

IEEE Trans. Consumer Electron.

View full text Add to dashboard Cite

In recent years Cyber-Physical Systems (CPS) and Industrial Internet of Things (IIoT) have gained significant attraction; however, it remains a vulnerable target for cyberattacks. Machine learning techniques have garnered interest in security applications due to their rapid processing capabilities and realtime predictions. However, imbalanced data distribution is a prevalent issue in IIoT environments, adversely affecting MLbased attack detection systems. In this work, we present a novel gametic heredity-based oversampling technique for addressing imbalanced data challenges in cybersecurity applications, specifically targeting IIoT systems. The proposed model enhances diversity in the minority classes by generating unique synthetic minority samples, creating diverse synthetic data while restricting instances to the minority class region. The proposed model outperforms complex and conventional methods in terms of precision, recall & F-Score while mitigating over-generalization by evenly distributing newly generated samples within minority class boundaries and regions. To validate the proposed model and verify its efficacy in identifying cyber threats, we used the UNSW-NB15 dataset. Simulation results demonstrate that the proposed model efficiently detects attacks with high performance compared to state-of-the-art techniques. Our research contributes to developing robust & efficient machine learning models for enhancing the security of IIoT systems while handling class imbalance issues.

show abstract

Leveraging Gametic Heredity in Oversampling Techniques to Handle Class Imbalance for Efficient Cyberthreat Detection in IIoT

Verma,

Breslin,

O’Shea

et al. 2024

IEEE Trans. Consumer Electron.

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A clustering and generative adversarial networks-based hybrid approach for imbalanced data classification

Cited by 1 publication

References 40 publications

Leveraging Gametic Heredity in Oversampling Techniques to Handle Class Imbalance for Efficient Cyberthreat Detection in IIoT

Leveraging Gametic Heredity in Oversampling Techniques to Handle Class Imbalance for Efficient Cyberthreat Detection in IIoT

Contact Info

Product

Resources

About