Artificial Intelligence Based Predictive Threat Hunting In The Field of Cyber Security

Artificial intelligence (AI) is a broad field of computer science that focuses on designing smart machines capable of performing tasks typically requiring human intelligence. Despite the fact that security solutions are growing progressively modern and stable, cyberattacks are still evolving and are at their extreme. The main reason is that conventional methods of malware detection fail. Cyber attackers are actively developing new ways to prevent defence programmes from infecting malware networks and servers. Most anti-malware and antivirus applications currently use signature-based detection to identify attacks, which is unsuccessful in detecting new threats. This is where Artificial Intelligence is most handy. The standardised models for threatened hunting and performance quantification from the start of hazard hunting to the end still allow methodological rigour and completeness to be studied remain undefined. The organised practise of hazard hunts seeks to disclose the presence of TTP in the field of detection that has not already been detected. In this study, a realistic and comprehensive model is outlined to detect attackers in six stages: aim, scale, equipment, planning, execution and input. This study describes Threat Hunting in an ecosystem as the constructive, analyst-driven scanning mechanism for attackers TTP. The model has been checked for real-world data sets using a variety of threats. The effectiveness and practicality of this research have been shown with and without a blueprint through danger hunts. In addition, the article presents an analysis of the concept of threat hunting based on data from Ukrainian electricity grid attacks in an online environment to highlight the effects of this model on threat hunting in a simulated environment. The findings of this analysis include an effective and repetitive way to search for and quantify honesty, coverage and rigour.