An Encoding Technique for CNN-based Network Anomaly Detection

An important challenge in the cyber-space is the effective identification of network anomalies, often caused by malicious activities. With the remarkable advances, machine learning algorithms have widely been studied for network intrusion and anomaly detection. In particular, deep learning based on neural network structures has recently been given a greater attention to deal with the growing complexity of data with higher dimensions and non-linearity. Convolutional Neural Networks (CNNs) is one of the widely employed deep learning methods. In this work, we introduce a new encoding technique that enhances the performance for the identification of anomalous events using a CNN structure. To evaluate, we utilize three different datasets for the extensive analysis. The experimental results show that our method consistently outperforms the gray-scale encoding technique previously proposed over the datasets employed in the evaluation.