Comparative Analysis of Deep Learning Models and Preprocessing Techniques for Anomaly Detection in Syslog

With the increasing number of cybersecurity attacks and their increasing complexity, it is necessary to adapt the detection methods to be able to prevent such attacks. The replacement of traditional detection methods based on machine learning with more advanced deep learning and neural network approaches is the crucial step for this. In this paper, we present a comparative analysis of different deep learning models for anomaly detection in syslog. We analysed existing datasets for system logs and compared several preprocessing methods. We evaluated different deep learning models on those preprocessed datasets to provide a comprehensive overview of the current state-of-the-art in cybersecurity. We achieved the best results with the CNN model with 0.999 F1-score on the BGL dataset showing the great potential in such techniques for the real-life models monitoring the system and detecting anomalies.