Industrial Control Systems (ICS) have played a catalytic role in enabling the 4th Industrial Revolution. ICS devices like Programmable Logic Controllers (PLCs), automate, monitor, and control critical processes in industrial, energy, and commercial environments. The convergence of traditional Operational Technology (OT) with Information Technology (IT) has opened a new and unique threat landscape. This has inspired defense research that focuses heavily on Machine Learning (ML) based anomaly detection methods that run on external IT hardware, which means an increase in costs and the further expansion of the threat landscape. To remove this requirement, we introduce the ICS machine learning inference framework (ICSML) which enables executing ML model inference natively on the PLC. ICSML is implemented in IEC 61131-3 code and provides several optimizations to bypass the limitations imposed by the domain-specific languages. Therefore, it works on every PLC without the need for vendor support. ICSML provides a complete set of components for creating full ML models similarly to established ML frameworks. We run a series of benchmarks studying memory and performance, and compare our solution to the TFLite inference framework. At the same time, we develop domain-specific model optimizations to improve the efficiency of ICSML. To demonstrate the abilities of ICSML, we evaluate a case study of a real defense for process-aware attacks targeting a desalination plant.

A state machine model is a mathematical model that groups all possible system occurrences, called states. The course emphasizes project-based learning, learning by doing. The goal of this course is to introduce an event-driven programming paradigm using simple and hierarchical state machines. After going through this course, you will be trained to apply the state machine approach to solve your complex embedded systems projects. If you are a beginner in the field of embedded systems, then you can take our courses in the below-mentioned order.

Robots are quickly bursting from the world of science fiction right into our own living rooms. The small-but-mighty BeagleBone Black embedded processor provides the power and capability to program your own robotic projects using its complete Linux development environment. Mastering BeagleBone Robotics lets you push your creativity to the limit through complex, diverse, and fascinating robotic projects right from scratch. Start off simple by building a tracked robot that moves, sees its environment, and navigates barriers. Go aquatic with a sailing robot that controls its rudder and sail, senses the direction of the wind, and plots its course using GPS.

Pixy is a smart vision sensor you can quickly "teach" to find objects. It saves you time by only outputting the object data you're interested in. A multitude of connection options means you can use Pixy with almost any microcontroller. It connects directly to Arduino with the included cable, and fully supports Raspberry Pi and BeagleBone Black with included software libraries. Included in the box is mounting hardware to attach Pixy to your robot creation.