ABSTRACT Industrial Control Systems monitor, automate, and operate complex infrastructure and processes that integrate into critical industrial sectors that affect our daily lives. With the increasing deployment of data network technologies in industrial control systems (ICSs), cybersecurity becomes a challenging problem in ICSs. During these ICS operation dangerous attacks, like machines malfunctions, increasing ambient temperature and unwanted gas particles may be released into the air also the attacks hazards. This project based on continuous monitoring ICS parameter such as load voltage-current, load condition (no-load/over-load), temperature, humidity and gas leakage, fire detection are monitored by wireless Zigbee technology. A microcontroller based system is used for collecting and storing data and making decision accordingly the data cyber-attacks machines and environmental malfunction. Extreme environment conditions are detrimental for human health. The communication system is reliable based on Zigbee, IEEE 802.15.4 standard. This is used for transmission between the hardware circuit fitted in the local site and the remote monitoring site (computer) through wireless devices. This project focuses on the use of process analytics to detect attacks in the industrial control infrastructure systems and compares the effectiveness of threshold value signature-based detection methods. The proposed work presents a pattern recognition algorithm aptly named as ‘‘Capturing-the-Invisible (CTI)’’ to find the hidden process in industrial control device logs and detect Behavior-based attacks being performed in real-time. This system is highly beneficial for rescue and protection of ICS and Industrial workers and equipment’s.

| DOI: 10.17148/IJIREEICE.2022.10585