Intrusion detection using ensemble methods and deep learning
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Date
2019-08
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G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand)
Abstract
In recent years Machine Learning and Deep Learning have played a crucial role in the field of cyber security. This research therefore intends to design IDS with a good mix of various ML and DL classifiers. It gives us an idea about which classifier gives the highest accuracy. The aim of this research is to classify whether the packet is a normal data packet or attacked data packet. Therefore the model of IDS should be implemented based on effective and significant ML and DL algorithms. To ensure network security, an effective intrusion detection system is required. Several ensemble methods like XG-Boost and LGBM have been developed in the past 4-5 years. These have not been exploited in the previous researches on anomaly detection. Our study will make use of these novel Gradient Boosting Decision Tree algorithms. XG-Boost and LGBM have proved to be the most productive techniques for several supervised and unsupervised learning tasks. In this research, we study several machine learning and deep learning classifiers and compare their performance. We have used the NSL KDD dataset to predict the probability of occurrence of 21 different classes of attacks on a network and three different categories of models - Linear Models including Logistic Regression and Stochastic Gradient Descent (SGD) classifier; Gradient Boosting Decision Tree ensembles including LightGBM (LGBM) and XG-Boost; and a Deep Neural Network (DNN) classifier and also trained a stacked model consisting of all these models as base learners. Finally, compared the performances of all the models for Network Intrusion Detection using the NSL-KDD dataset and have drawn useful conclusions. The simulation result shows that Light GBM, XGBoost, and stacked classifiers outperform with high accuracy as compared to Logistic regression, Stochastic Gradient Descent Classifier and Deep Neural network. All of these are predictive analysis techniques.
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