Towards Building Intrusion Detection Systems for Multivariate Time-Series Data

Recent network intrusion detection systems have employed machine learning and deep learning algorithms to defend against dynamically evolving network attacks. While most previous studies have focused on detecting attacks which can be determined based on a single time instant, few studies have paid attention to subsequence outliers, which require inspecting consecutive points in time for detection. To address this issue, this paper applies a time-series anomaly detection method in an unsupervised learning manner. To this end, we converted the UNSW-NB15 dataset into the time-series data. We carried out a preliminary evaluation to test the performance of the anomaly detection on the created time-series network dataset as well as on a time-series dataset obtained from sensors. We analyze and discuss the results.