Fine Tuned Deep Neural Networks for Intrusion Detection System

Abstract

Intrusion detection system is used to gather and analyze data from networks for possible threats identification. It is used to record all significant events within network and takes corrective action when threats are detected. Some existing intrusion detection systems have experienced false positive or negative alarms. Recently, deep neural networks are being widely used to implement intrusion detection system to increase accuracy and reduce false alarms. Deep Neural networks are encouraged by the architecture depth of the human brain. These can possibly remove better portrayals from the information to make much better models. Tuning of hyper parameter of complex and computationally expensive deep networks is essential to advance classification accuracy by reducing training time. This hyper parameter tuning is also known as automated machine learning. In this paper, a deep neural network has been used for intrusion detection system. Different architecture of Multilayer perceptron has verified to learn best performance on intrusion detection system. Different hyper parameters of multilayer perceptron have tuned using different optimization techniques. Small to complex architecture of Perceptrons have used to carry out experiments. It was observed that smaller or simpler Neural Network is able to capture the function representation more closely than the more complex one. More complex deep networks with higher capacities are more prone to over fit. Hyper parameters tuning techniques such as dropout and regularization have helped to reduce complexities of model. It is also found that hyper parameter optimization techniques such as Bayesian and Hyperband optimization reduce considerable time and effort. These optimization techniques offer accuracies of 83% and 82% respectively on test dataset.