An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning

Hanafi, Hanafi (2022) An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning. [Research]

Text ICAITI 2022 Submission 148 (1).pdf - Published Version Download (169kB)

Abstract

Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness up to more than 2% in NSLKDD Dataset. Moreover, the use of SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection.

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