NEW FRAMEWORK FOR AUTOMATIC CYBER THREAT SYSTEM USING ARTIFICIAL INTELLIGENCE PROCESSING

Abstract

 Critical infrastructure (CI) typically refers to the essential physical and virtual systems, assets, and services that are vital for the functioning and well-being of a society, economy, or nation. Cyber threats pose significant risks to organizations and individuals, necessitating the development of advanced systems for threat intelligence modeling and identification. Cyber strategists at a national level require AI-based decision support systems for deciding a country’s cyber posture or preparedness. Theproposes an AI-based solution that autonomously collects multidimensional cyber-attack data on social media posts on cyber-related outcry. The proposed system provides critical analytical capability in the cyber-threat spectrum and uses sophisticated AI based algorithms for anomaly detection, prediction, sentiment analysis, location detection, translation, A wide variety of Cyber Threat Information (CTI) is used by Security Operation Centres (SOCs) to perform validation of security incidents and alerts. Security experts manually define different types of rules and scripts based on CTI to perform validation tasks. We have performed experiments on real healthcare ecosystems in Fraunhofer Institute for Biomedical Engineering, considering in particular three different healthcare scenarios, namely implantable medical devices with the purpose of demonstrating the feasibility of our approach. s. We also cover how these techniques can address diverse cybersecurity concerns such as threat detection, mitigation, prediction, diagnosis for root cause findings, and so on in different CI sectors, such as energy, defence, transport, health, water, agriculture,This approach offers more insight into the nature of the danger and suggests possible ways to address it.