Network traffic analysis can raise privacy concerns due to its ability to reveal sensitive information about individuals and organizations. This paper proposes a privacy-preserving Block-chained AutoML Network Traffic Analyzer (BANTA). The system securely stores network traffic logs in a decentralized manner, providing transparency and security. Differential privacy algorithms protect sensitive information in the network flow logs while allowing administrators to analyze network traffic without the risk of leakages. The BANTA uses blockchain technology, where smart contracts automate the process of network traffic analysis, and a multi-signature system ensures the system’s security, safety, and reliability. The proposed approach was evaluated using a real-world network traffic dataset. The results demonstrate the system’s high accuracy and real-time anomaly detection capabilities, which makes it well-suited for scalable cybersecurity operations. The system’s privacy protection, decentralized storage, automation, multi-signature system, and real-world effectiveness ensure that the organization’s data is private, secure, and effectively protected from cyber threats, which are the most vexing issue of modern cyber-physical systems.

Developing intelligent, interoperable Cyber Threat Information (CTI) sharing technologies can help build strong defences against modern cyber threats. CTIs allow the community to share information about cybercriminals' threats and vulnerabilities and countermeasures to defend themselves or detect malicious activity. A crucial need for success is that the data connected to cyber risks be understandable, organized, and of good quality. The receiving parties may grasp its content and utilize it effectively. This article describes an innovative cyber threat intelligence management platform (CTIMP) for industrial environments, one of the Cyber-pi project's significant elements. The suggested architecture, in particular, uses cyber knowledge from trusted public sources and integrates it with relevant information from the organization's supervised infrastructure in an entirely interoperable and intelligent way. When combined with an advanced visualization mechanism and user interface, the services mentioned above provide administrators with the situational awareness they require while also allowing for extended cooperation, intelligent selection of advanced coping strategies, and a set of automated self-healing rules for dealing with threats.

Developing intelligent, interoperable Cyber Threat Information (CTI) sharing technologies can help build strong defences against modern cyber threats. CTIs allow the community to share information about cybercriminals' threats and vulnerabilities and countermeasures to defend themselves or detect malicious activity. A crucial need for success is that the data connected to cyber risks be understandable, organized, and of good quality. The receiving parties may grasp its content and utilize it effectively. This article describes an innovative cyber threat intelligence management platform (CTIMP) for industrial environments, one of the Cyber-pi project's significant elements. The suggested architecture, in particular, uses cyber knowledge from trusted public sources and integrates it with relevant information from the organization's supervised infrastructure in an entirely interoperable and intelligent way. When combined with an advanced visualization mechanism and user interface, the services mentioned above provide administrators with the situational awareness they require while also allowing for extended cooperation, intelligent selection of advanced coping strategies, and a set of automated self-healing rules for dealing with threats.