Automated Threat Detection and Response System

The abstracts unitedly explored the consolidation of stirred word AI and advanced technologies in addressing modern day cybersecurity challenges. They emphasize the necessity for proactive and automated responses to dynamic threats, whether in military, corporate, or cloud-based environments. Key topics include the deployment of AI for rapid threat detection using machine learning (ML) and deep learning (DL) to identify anomalies and zero-day attacks, risk assessment with Bayesian networks, and incident response through reinforcement learning and natural language processing (NLP). Several papers highlight AI's role in reducing human dependency by automating tasks like isolating compromised systems and processing threat intelligence. The studies also discuss innovative tools like Automated Threat Response using Intelligent Agents (ATRIA) for military use and systems like SCERM for refining cyber threat intelligence (CTI) reports. Cloud security is another critical focus, with proposed solutions such as Slingshot for real-time detection and mitigation of threats on platforms like AWS and GCP. Additionally, the research examines challenges in integrating threat intelligence, sharing platforms with policy-controlled systems and enhancing the utility of Structured Threat Information Expression (STIX) for efficient threat management. Overall, the abstracts underscore the importance of harnessing AI-driven tools, such as predictive analytics, graph databases, and real-time decision-making systems, to improve cybersecurity resilience and efficiency in an increasingly complex digital environment.