Industrial Control System

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Feds Uncover a ‘Swiss Army Knife’ for Hacking Industrial Control Systems


Author: Andy Greenberg

Topics: cybersecurity, industrial control system

Organizations: Dragos

On Wednesday, the Department of Energy, the Cybersecurity and Infrastructure Security Agency, the NSA, and the FBI jointly released an advisory about a new hacker toolset potentially capable of meddling with a wide range of industrial control system equipment. More than any previous industrial control system hacking toolkit, the malware contains an array of components designed to disrupt or take control of the functioning of devices, including programmable logic controllers (PLCs) that are sold by Schneider Electric and OMRON and are designed to serve as the interface between traditional computers and the actuators and sensors in industrial environments. Another component of the malware is designed to target Open Platform Communications Unified Architecture (OPC UA) servers—the computers that communicate with those controllers.

Dragos says the malware has the ability to hijack target devices, disrupt or prevent operators from accessing them, permanently brick them, or even use them as a foothold to give hackers access to other parts of an industrial control system network. He notes that while the toolkit, which Dragos calls “Pipedream,” appears to specifically target Schneider Electric and OMRON PLCs, it does so by exploiting underlying software in those PLCs known as Codesys, which is used far more broadly across hundreds of other types of PLCs. This means that the malware could easily be adapted to work in almost any industrial environment. “This toolset is so big that it’s basically a free-for-all,” Caltagirone says. “There’s enough in here for everyone to worry about.”

Read more at WIRED

What is the future of Control Systems? The Evolution of Control Systems.

Artificial intelligence optimally controls your plant


Topics: energy consumption, reinforcement learning, machine learning, industrial control system

Organizations: Siemens

Until now, heating systems have mainly been controlled individually or via a building management system. Building management systems follow a preset temperature profile, meaning they always try to adhere to predefined target temperatures. The temperature in a conference room changes in response to environmental influences like sunlight or the number of people present. Simple (PI or PID) controllers are used to make constant adjustments so that the measured room temperature is as close to the target temperature values as possible.

We believe that the best alternative is learning a control strategy by means of reinforcement learning (RL). Reinforcement learning is a machine learning method that has no explicit (learning) objective. Instead, an “agent” with as complete a knowledge of the system state as possible learns the manipulated variable changes that maximize a “reward” function defined by humans. Using algorithms from reinforcement learning, the agent, meaning the control strategy, can be trained from both current and recorded system data. This requires measurements for the manipulated variable changes that have been carried out, for the (resulting) changes to the system state over time, and for the variables necessary for calculating the reward.

Read more at Siemens Ingenuity

Why resources companies are looking to evented APIs


Author: Ryan Grondal

Topics: industrial control system

Vertical: Mining, Petroleum and Coal

Organizations: MuleSoft

Resources companies that want to get the most value from their data will process it the instant that it is created. The longer that data is left unprocessed, the more it diminishes in value. Operational excellence can be driven by evented APIs that can produce, detect, consume, and react to events occurring within the technology ecosystem.

Evented APIs can be applied to our example use case to deliver an autonomous feedback loop that incorporates smarter decision making in real-time.

Read more at MuleSoft Blog

Evolving control systems are key to improved performance


Author: Sean Sims

Topics: digital twin, industrial control system, edge computing

Organizations: Emerson

For decades, the control system was constrained by physical hardware: hardwired input/output (I/O) layouts, connected controllers and structured architectures including dedicated networks and server configurations. Now, the lower cost of processing power and sensing, the evolution of network and wireless infrastructure, and distributed architectures (including the cloud) are unlocking new opportunities in control systems. Additionally, emerging standards for plug-and-produce, such as advanced physical layer (APL) and modular type package (MTP) interfaces, will drive significant changes in the way plants design and use control systems over the next decade.

Read more at Control Engineering

Evolution of control systems with artificial intelligence


Authors: Kence Anderson, Winston Jenks, Prabu Parthasarathy

Topics: AI, industrial control system

Organizations: Microsoft, John Wood Group

Control systems have continuously evolved over decades, and artificial intelligence (AI) technologies are helping advance the next generation of some control systems.

The proportional-integral-derivative (PID) controller can be interpreted as a layering of capabilities: the proportional term points toward the signal, the integral term homes in on the setpoint and the derivative term can minimize overshoot.

Although the controls ecosystem may present a complex web of interrelated technologies, it can also be simplified by viewing it as ever-evolving branches of a family tree. Each control system technology offers its own characteristics not available in prior technologies. For example, feed forward improves PID control by predicting controller output, and then uses the predictions to separate disturbance errors from noise occurrences. Model predictive control (MPC) adds further capabilities to this by layering predictions of future control action results and controlling multiple correlated inputs and outputs. The latest evolution of control strategies is the adoption of AI technologies to develop industrial controls.

Read more at Control Engineering

Sensor Fusion: The Swiss Army Knife of Digitalization


Author: David Miller

Topics: industrial control system, digital transformation, edge computing, predictive maintenance

Organizations: Balluff, Bosch Rexroth, Emerson, Omron

With the proper communication protocols and network architecture in place, smart sensor technology and the data it provides can be the bulwark on which digital transformation is built.

If industrial control systems are the brains of a plant, then sensors are its eyes and ears. Simply put, without sensors there would be nothing for SCADA, DCS, or PLCs to respond to. That’s why increasingly intelligent or ‘smart’ sensors packing more onboard processing power, the ability to monitor new variables, and digital communication capabilities are playing such an important role in helping plant operators and enterprise level planners alike to see better and respond to problems with more finesse.

Read more at Automation World