An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks

Registration ID: IJNRD_223443

Published ID: IJNRD2406193

DOI: Click Here to Get

J.DELPHIN , Dr.M.JANAKI

Anomaly detection, generative adversarial network (GAN), network intrusion detection system (NIDS), network security.

Ascommunicationtechnologyadvances,variousand heterogeneous data are communicated in distributed environ- ments through network systems. Meanwhile, along with the development of communication technology, the attack surfacehas expanded, and concerns regarding network security have increased. Accordingly, to deal with potential threats, researchonnetworkintrusiondetectionsystems(NIDSs)hasbeenactively conducted.AmongthevariousNIDStechnologies,recentinterest is focused on artificial intelligence (AI)-based anomaly detection systems, and various models have been proposed to improve the performanceofNIDS.However,therestillexiststheproblem of data imbalance, in which AI models cannot sufficiently learn malicious behavior and thus fail to detect network threats accu- rately. In this study, we propose a novel AI-based NIDS that can efficiently resolve the data imbalance problem and improve the performance of the previous systems. To address the aforemen- tioned problem, we leveraged a state-of-the-art generative model that could generate plausible synthetic data for minor attack traffic. In particular, we focused on the reconstruction error and Wassersteindistance-basedgenerativeadversarialnetworks,and autoencoder-driven deep learning models. To demonstrate the effectiveness of our system, we performed comprehensive evalu- ationsovervariousdatasetsanddemonstratedthattheproposed systemssignificantlyoutperformedthepreviousAI-basedNIDS.

Published Paper   E-Certificate

"An Enhanced AI-Based Network Intrusion Detection System Using Generative Adversarial Networks", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 6, page no.b931-b939, June-2024, Available :https://ijnrd.org/papers/IJNRD2406193.pdf

Volume 9 Issue 6, June-2024

Pages : b931-b939

Paper Reg. ID: IJNRD_223443

Published Paper Id: IJNRD2406193

Downloads: 000121195

Research Area: Computer Science & Technology 

Country: Karaikudi/ Sivaganga, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2406193.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2406193

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

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