GRAVITAS: Graphical Reticulated Attack Vectors for Internet-of-Things Aggregate Security
Jacob Brown, Tanujay Saha, Niraj K. Jha

TL;DR
GRAVITAS is a comprehensive risk management system for IoT and CPS that uses machine learning to identify undiscovered attack vectors and optimize defense placement, enhancing security at scale.
Contribution
It introduces a novel machine learning-based model to extrapolate unknown exploits and optimize defense strategies in complex IoT/CPS networks.
Findings
Identifies previously overlooked attack vectors using ML.
Provides optimized defense placement recommendations.
Quantifies security improvements and cost efficiency.
Abstract
Internet-of-Things (IoT) and cyber-physical systems (CPSs) may consist of thousands of devices connected in a complex network topology. The diversity and complexity of these components present an enormous attack surface, allowing an adversary to exploit security vulnerabilities of different devices to execute a potent attack. Though significant efforts have been made to improve the security of individual devices in these systems, little attention has been paid to security at the aggregate level. In this article, we describe a comprehensive risk management system, called GRAVITAS, for IoT/CPS that can identify undiscovered attack vectors and optimize the placement of defenses within the system for optimal performance and cost. While existing risk management systems consider only known attacks, our model employs a machine learning approach to extrapolate undiscovered exploits, enabling us…
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