| Title | Unsupervised Time-Series Based Anomaly Detection in ICS/SCADA Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Tekeoglu, Ali, Bekiroglu, Korkut, Chiang, Chen-Fu, Sengupta, Sam |
| Conference Name | 2021 International Symposium on Networks, Computers and Communications (ISNCC) |
| Date Published | Nov. 2021 |
| Publisher | IEEE |
| ISBN Number | 978-1-6654-0304-7 |
| Keywords | Air gaps, anomaly detection, composability, Cyber-physical systems, Human Behavior, ICS Anomaly Detection, ICS SCADA Network Security, industrial control, integrated circuits, Metrics, Network security, pubcrawl, Real-time Systems, resilience, Resiliency, Secure Water Treatment Testbed Dataset, Sensor systems, telecommunication traffic, time-series |
| Abstract | Traditionally, Industrial Control Systems (ICS) have been operated as air-gapped networks, without a necessity to connect directly to the Internet. With the introduction of the Internet of Things (IoT) paradigm, along with the cloud computing shift in traditional IT environments, ICS systems went through an adaptation period in the recent years, as the Industrial Internet of Things (IIoT) became popular. ICS systems, also called Cyber-Physical-Systems (CPS), operate on physical devices (i.e., actuators, sensors) at the lowest layer. An anomaly that effect this layer, could potentially result in physical damage. Due to the new attack surfaces that came about with IIoT movement, precise, accurate, and prompt intrusion/anomaly detection is becoming even more crucial in ICS. This paper proposes a novel method for real-time intrusion/anomaly detection based on a cyber-physical system network traffic. To evaluate the proposed anomaly detection method's efficiency, we run our implementation against a network trace taken from a Secure Water Treatment Testbed (SWAT) of iTrust Laboratory at Singapore. |
| URL | https://ieeexplore.ieee.org/document/9615827 |
| DOI | 10.1109/ISNCC52172.2021.9615827 |
| Citation Key | tekeoglu_unsupervised_2021 |
Unsupervised Time-Series Based Anomaly Detection in ICS/SCADA Networks