ABSTRACT More and more network traffic data have brought great challenge to traditional intrusion detection system. The detection performance is tightly related to selected features and classifiers, but traditional feature selection algorithms and classification algorithms can't perform well in massive data environment. Also the raw traffic data are imbalanced, which has a serious impact on the classification results. In this paper, we propose a novel network intrusion detection model utilizing convolutional neural networks (CNNs). We use CNN to select traffic features from raw data set automatically, and we set the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem. The model not only reduces the false alarm rate (FAR) but also improves the accuracy of the class with small numbers. To reduce the calculation cost further, we convert the raw traffic vector format into image format. We use the standard NSL-KDD data set to evaluate the performance of the proposed CNN model. The experimental results show that the accuracy, FAR, and calculation cost of the proposed model perform better than traditional standard algorithms. It is an effective and reliable solution for the intrusion detection of a massive network. INDEX TERMS Network intrusion detection, convolutional neural networks, image data format conversion, cost function weight, imbalanced dataset.
KEHE WU et al. REF used CNN to select traffic features from raw data set automatically, and set the cost function weight coefficient of each class based on its numbers to solve the imbalanced data set problem.
A Novel Intrusion Detection Model for a Massive Network Using Convolutional Neural Networks
IEEE Access
IEEE Access
Computer Science