KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation

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dc.contributor.authorKotal, Anantaa
dc.contributor.authorLuton, Brandon
dc.contributor.authorJoshi, Anupam
dc.date.accessioned2024-07-12T14:57:16Z
dc.date.available2024-07-12T14:57:16Z
dc.date.issued2024-05-26
dc.description.abstractIn the realm of IoT/CPS systems connected over mobile networks, traditional intrusion detection methods analyze network traffic across multiple devices using anomaly detection techniques to flag potential security threats. However, these methods face significant privacy challenges, particularly with deep packet inspection and network communication analysis. This type of monitoring is highly intrusive, as it involves examining the content of data packets, which can include personal and sensitive information. Such data scrutiny is often governed by stringent laws and regulations, especially in environments like smart homes where data privacy is paramount. Synthetic data offers a promising solution by mimicking real network behavior without revealing sensitive details. Generative models such as Generative Adversarial Networks (GANs) can produce synthetic data, but they often struggle to generate realistic data in specialized domains like network activity. This limitation stems from insufficient training data, which impedes the model’s ability to grasp the domain’s rules and constraints adequately. Moreover, the scarcity of training data exacerbates the problem of class imbalance in intrusion detection methods. To address these challenges, we propose a Privacy-Driven framework that utilizes a knowledge-infused Generative Adversarial Network for generating synthetic network activity data (KiNETGAN). This approach enhances the resilience of distributed intrusion detection while addressing privacy concerns. Our Knowledge Guided GAN produces realistic representations of network activity, validated through rigorous experimentation. We demonstrate that KiNETGAN maintains minimal accuracy loss in downstream tasks, effectively balancing data privacy and utility.
dc.description.urihttp://arxiv.org/abs/2405.16476
dc.format.extent6 pages
dc.genrejournal articles
dc.genrepreprints
dc.identifierdoi:10.13016/m2jadf-xww0
dc.identifier.urihttp://hdl.handle.net/11603/34866
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Center for Cybersecurity
dc.rightsCC0 1.0 UNIVERSAL
dc.rights.urihttps://creativecommons.org/publicdomain/zero/1.0/
dc.subjectComputer Science - Cryptography and Security
dc.subjectComputer Science - Machine Learning
dc.titleKiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0003-1818-9705
dcterms.creatorhttps://orcid.org/0000-0002-8641-3193

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