With Machine Learning STI converts data into valuable information. With this information automatic decisions are made. Ideal for solutions for querying information and pattern recognition. ‘Intelligent’ algorithms are capable of learning from enormous amounts of input – and converting this into practical output, whether or not as a result of a new specific dataset that must be connected to a particular output.
STI uses machine learning for detection of deviant data in cybersecurity solutions and for analysing large amounts of data for production process optimization.
Since the machine learning models are built within STI, it is possible to adjust the models directly and define new projects where the data scientist designs the database and describes the required data. After collecting the desired data, the data is then analysed with the AI models.
Some of the most used applications of machine learning technology are for tasks that are almost always identical in execution, so repetitive work, with only a limited number of outcomes possible. Based on variables in the input, a system can constantly develop itself to perform its task even better – and to be able to deliver reliable output.
In theory, therefore, any industry that benefits from a simplification of difficult to automate repetitive tasks could use machine learning. An algorithm can classify the often subjective and / or multiple variables input into usable output, which would result in a huge gain of time.
Data Value Creations
The team starts by making process descriptions of the operational process. The different data touch points are identified and subsequently determined which data is needed to achieve the named success criteria. Which data is then available and which has yet to be obtained with the installation of additional IoT solutions. E.g. sensors are linked to the newly developed security system and data is collected.
The data scientist of the team works out the design of the database and the data and determines how this can be obtained. This understanding value phase is concluded with a Business Case for Change. This describes the possibilities for process optimization and the required resources to realize this.
After collecting the necessary data, the value creation is started. An “Artificial Intelligent” model is set up with which the data is analysed and decisions made by the computer. Based on the findings, process optimization is realized and realized.
The following applies: The created value or process improvements must be demonstrated and documented.
Location Based security
Security Tools International has developed a (cyber) security tool.
Within the OT environment, the digital transformation within production companies started years ago with the introduction of Programmable Logic Controllers (“PLCs”).
Securing these ‘design for purpose’ control systems is very costly and the development of Industry 4.0, the automation of production and data exchange, calls for specific and above all secure solutions: Location Based (cyber) Security.
This is an intrusion detection solution, whereby existing infrastructure and operational processes are placed under security, both physically and digitally, without any modifications. The system detects deviant behaviour in the network based on Machine Learning and is integrated with a cyber resilience platform. Physical access control on location enables protection against threats and risks from the inside.