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Development of an Intrusion Detection System Leveraging Deep Learning Model Classification
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The implementation of Deep Learning in development of models to act as interventions for addressing the continuously evolving spate of cybersecurity issues has become a noteworthy paradigm. This occurs since cyber attacks could be modeled or represented in terms of data records which can serve as bases for developing intrusion detection systems. This paper proposes an intrusion detection system that leverages deep learning techniques for attack classification. Two deep learning models were developed, a Deep Neural Network (DNN) with ReLU activation as well as Tabular model using the fastai deep learning library. The NSL-KDD benchmark dataset was imported and preprocessed for each model development. After evaluating the models, the fastai model with accuracy of 84 percent surpassed the other DNN with accuracy of 79 percent on NSL-KDD test data.
Advances in Artificial Intelligence and Machine Learning
Title: Development of an Intrusion Detection System Leveraging Deep Learning Model Classification
Description:
The implementation of Deep Learning in development of models to act as interventions for addressing the continuously evolving spate of cybersecurity issues has become a noteworthy paradigm.
This occurs since cyber attacks could be modeled or represented in terms of data records which can serve as bases for developing intrusion detection systems.
This paper proposes an intrusion detection system that leverages deep learning techniques for attack classification.
Two deep learning models were developed, a Deep Neural Network (DNN) with ReLU activation as well as Tabular model using the fastai deep learning library.
The NSL-KDD benchmark dataset was imported and preprocessed for each model development.
After evaluating the models, the fastai model with accuracy of 84 percent surpassed the other DNN with accuracy of 79 percent on NSL-KDD test data.
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