Biblio

Cyber breaches can result in disruption to business operations, reputation damage as well as directly affecting the financial stability of the targeted corporations, with potential impacts on future profits and stock values. Automatic network-stream monitoring becomes necessary for cyber situation awareness, and time-series anomaly detection plays an important role in network stream monitoring. This study surveyed recent research on time-series analysis methods in respect of parametric and non-parametric techniques, and popular machine learning platforms for data analysis on streaming data on both single server and cloud computing environments. We believe it provides a good reference for researchers in both academia and industry to select suitable (time series) data analysis techniques, and computing platforms, dependent on the data scale and real-time requirements.

We outline an anomaly detection method for industrial control systems (ICS) that combines the analysis of network package contents that are transacted between ICS nodes and their time-series structure. Specifically, we take advantage of the predictable and regular nature of communication patterns that exist between so-called field devices in ICS networks. By observing a system for a period of time without the presence of anomalies we develop a base-line signature database for general packages. A Bloom filter is used to store the signature database which is then used for package content level anomaly detection. Furthermore, we approach time-series anomaly detection by proposing a stacked Long Short Term Memory (LSTM) network-based softmax classifier which learns to predict the most likely package signatures that are likely to occur given previously seen package traffic. Finally, by the inspection of a real dataset created from a gas pipeline SCADA system, we show that an anomaly detection scheme combining both approaches can achieve higher performance compared to various current state-of-the-art techniques.