Intrusion Detection Systems with GPU-Accelerated Deep Neural Networks and Effect of the Depth

Abstract

With the extended use of the Internet, which connects millions of computers across the world, there is a growing number and types of intrusions which complicate ensuring the security of information and computers. Although Firewalls and rule/signature base Intrusion Detection Systems (IDSs) are used as the first line of the defense of networks, they cannot be sufficient for detecting the zero-day type attacks, which are not previously encountered. For this type of attacks, Anomaly-Based Intrusion detection systems arise as an acceptable solution which models the normal communication behavior of the network and identifies the others as a suspicious transaction. To classify the normal behavior, usage of neural networks and machine learning approaches are accepted as powerful solutions. However, due to the lack of computation power, generally single hidden layer approach is preferred. With the enhancement of the parallel computation technology, especially in Graphics Processing Units (GPUs), it will be easy to implement a multi-layer approach in Deep Neural Network concept which has a great deal of attention within Deep Learning approach. Therefore, better accuracy rate could be reached. In this paper, we aimed to implement a Deep Neural Network-based Intrusion Detection System. Moreover, we also study the performance of the proposed model in binary classification with a different number of layers, neurons and parameters. Additionally, the acceleration of the GPU usage is also measured and presented with a comparison. To measure the performance of the proposed system the NSL-KDD data set, which is a 'cleaned' data set of the KDD data set, is preferred. The experimental results showed that the proposed multi-layer Deep Neural Network model produces an acceptable performance in its classification with a high accuracy rate with the design of a 64x32 hidden layer structure depending on the data set NSL-KDD.