In the last decade market and cost pressures have driven significant technological advances in automation and industrial connectivity across all aspects of petroleum extraction, pipeline transport and refining. While technological advances are delivering business benefits, systems are now exposed to more cyber risks than ever before.
Yet, according to a 2017 survey by the Ponemon Institute, the deployment of cyber security measures in the oil and gas industry isn’t keeping pace with the growth of digitalization in operations.
One way to overcome the ICS cyber security gap is to utilize next generation technology that leverages machine learning and artificial intelligence (AI) to deal with system complexity and deliver immediate benefits. Let’s take a look at two examples of how a passive ICS anomaly detection and monitoring solution secures pipeline networks.
One of the findings of the recent SANS report “Securing Industrial Control Systems – 2017” is that the number one technology industrial organizations are looking to implement over the next 18 months is intrusion detection.
Up until recently, detecting anomalies on ICS networks that might be caused by a cyberattack has been ”mission impossible.” That’s because such networks typically include equipment from a wide assortment of vendors, run thousands of real-time processes and generate huge volumes of data. Analyzing and monitoring this data to detect anomalies was very difficult.
The good news is that a new generation of ICS cyber security tool is available for industrial intrusion detection. This article describes how our product, SCADAguardian does it, and gives an example of how it would detect and counter a cyberattack on a regional control center of an electric power utility.