Deep Neural Networks based Meta-Learning for Network Intrusion Detection Article Swipe

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Anabia Sohail , Bibi Ayisha , Irfan Hameed , Muhammad Mohsin Zafar , Asifullah Khan · YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2302.09394

The digitization of different components of industry and inter-connectivity among indigenous networks have increased the risk of network attacks. Designing an intrusion detection system to ensure security of the industrial ecosystem is difficult as network traffic encompasses various attack types, including new and evolving ones with minor changes. The data used to construct a predictive model for computer networks has a skewed class distribution and limited representation of attack types, which differ from real network traffic. These limitations result in dataset shift, negatively impacting the machine learning models' predictive abilities and reducing the detection rate against novel attacks. To address the challenges, we propose a novel deep neural network based Meta-Learning framework; INformation FUsion and Stacking Ensemble (INFUSE) for network intrusion detection. First, a hybrid feature space is created by integrating decision and feature spaces. Five different classifiers are utilized to generate a pool of decision spaces. The feature space is then enriched through a deep sparse autoencoder that learns the semantic relationships between attacks. Finally, the deep Meta-Learner acts as an ensemble combiner to analyze the hybrid feature space and make a final decision. Our evaluation on stringent benchmark datasets and comparison to existing techniques showed the effectiveness of INFUSE with an F-Score of 0.91, Accuracy of 91.6%, and Recall of 0.94 on the Test+ dataset, and an F-Score of 0.91, Accuracy of 85.6%, and Recall of 0.87 on the stringent Test-21 dataset. These promising results indicate the strong generalization capability and the potential to detect network attacks.

Type

preprint

Language

en

Landing Page

http://arxiv.org/abs/2302.09394

PDF

https://arxiv.org/pdf/2302.09394

OA Status

green

Cited By

5

Related Works

10

OpenAlex ID

https://openalex.org/W4321472257

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OpenAlex ID

https://openalex.org/W4321472257

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DOI

https://doi.org/10.48550/arxiv.2302.09394

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Title

Deep Neural Networks based Meta-Learning for Network Intrusion Detection

Work title

Type

preprint

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Language

en

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Publication year

2023

Year of publication

Publication date

2023-02-18

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Authors

Anabia Sohail, Bibi Ayisha, Irfan Hameed, Muhammad Mohsin Zafar, Asifullah Khan

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Landing page

https://arxiv.org/abs/2302.09394

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PDF URL

https://arxiv.org/pdf/2302.09394

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Open access

Yes

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OA status

green

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OA URL

https://arxiv.org/pdf/2302.09394

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Concepts

Computer science, Artificial intelligence, Machine learning, Autoencoder, Benchmark (surveying), Ensemble learning, Deep learning, Artificial neural network, Feature (linguistics), Feature vector, Intrusion detection system, Data mining, Pattern recognition (psychology), Geography, Philosophy, Linguistics, Geodesy

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Cited by

5

Total citation count in OpenAlex

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2025: 3, 2024: 2

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Related works (count)

10

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