Optimizing Network Security using LSTM Algorithm for Traffic Classification on UNSWNB15 and KDDCUP99 Dataset
Keywords:
Machine Learning, Deep Learning, Long-Short Term Memory, bidirectional LSTM, UNSWNB15, KDDCUP99Abstract
Various systems depend on the capability to classify network traffic for tasks such as detecting intrusions, enforcing policies, and managing traffic. Machine Learning (ML) and especially Deep Learning (DL) based classifiers have shown exceptional accuracy in classifying network traffic, despite the fact that many applications encrypt their network data and others change their port numbers constantly. This paper proposes a classification approach using graph convolution and Long-Short Term Memory (LSTM) to handle coupled network data flows. In order to analyse the spatial topological and temporal properties of the LSTM, the traffic flow data has to be preprocessed. Ultimately, the approach is evaluated on a portion of the UNSWNB15 and KDDCUP99 datasets to quantify its effectiveness. The proposed methodology has shown its ability to effectively derive potential attributes from network traffic data via successful experimentation. The recommended strategy is shown to be effective and performs better than other methods such as feature selection, bidirectional LSTM (BiDLSTM), and CNN-LSTM in terms of classification performance.
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