Internet of Things (IoT) devices are not only finding increasing use in ordinary households, but they have also become a key element for the Industry 4.0 concept. The implementation of industrial IoT devices into production streamlines the production process and reduces production costs. On the other hand, connected IoT devices bring new security risks to production and expose an industrial environment to new types of attacks. The article analyzes the vulnerability of the production line with implemented industrial IoT devices with consideration of a possible Distributed Denial-of-service (DDoS) attack led by attackers from the internet. Various types of DDoS attacks abusing the presence of IoT devices in the system were performed on an automated production line implementing sorting, preparation, and dosing of bulk and liquid materials for filling into containers. The leading attacks caused failure of the production line during the production, as well as the dysfunction of communication with IoT devices. The article also demonstrates the implementation of countermeasures against DDoS attacks and possible strategies to protect and mitigate such attacks on the production line.

Today, Industrial Internet of Things (IIoT) devices are very often used to collect manufacturing process data. The integration of industrial data is increasingly being promoted by the Open Platform Communications United Architecture (OPC UA). However, available IIoT devices are limited by the features they provide; therefore, we decided to design an IIoT device taking advantage of the benefits arising from OPC UA. The design procedure was based on the creation of sequences of steps resulting in a workflow that was transformed into a finite state machine (FSM) model. The FSM model was transformed into an OPC UA object, which was implemented in the proposed IIoT. The OPC UA object makes it possible to monitor events and provide important information based on a client’s criteria. The result was the design and implementation of an IIoT device that provides improved monitoring and data acquisition, enabling improved control of the manufacturing process.

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