Big Data Driven Cyber Threat Intelligence Framework for U.S. Critical Infrastructure Protection

Abstract

The key infrastructure systems found throughout the U.S.—energy, transportation, healthcare, and water systems—are becoming ever more dependent on connected virtual online networks, thus increasing their vulnerability to both more ubiquitous and sophisticated cyber threats. Traditional security measures are unable to adapt to the volume, velocity, and variety of data generated by today’s cyber-attacks. This paper offers a Big Data-Driven Cyber Threat Intelligence Framework (BD-CTIF) that simultaneously takes advantage of real-time networking and IoT data-sharing, distributed analytics, and AI-based anomaly detection at the speed of business to provide proactive threat intelligence for U.S. critical infrastructures. Tests showed low latency, and high accuracy of detection demonstrating the framework's utility for protecting U.S. national critical infrastructure. The proposed BDA methods borrowed from artificial intelligence (AI), machine learning (ML), natural language processing (NLP), and then employ deep knowledge to cite massive data sets for anomalies and respond to potential threats with high accuracy. This paper reviews the interrelationship of machine learning, artificial intelligence, and biological warfare with fresh insights into how those converge in relationship to cyber security for critical infrastructures. A review of the advantages, challenges, and options for operational use are considered in the discussion. Ultimately, this work demonstrates unrealized potential for any of the areas of artificial intelligence (AI).