HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for
  Root Cause Analysis with GPT-assisted Mitigation Suggestion
Yu-Zheng Lin Muntasir Mamun Muhtasim Alam Chowdhury Shuyu Cai Mingyu Zhu Banafsheh Saber Latibari Kevin Immanuel Gubbi Najmeh Nazari Bavarsad Arjun Caputo Avesta Sasan Houman Homayoun Setareh Rafatirad Pratik Satam Soheil Salehi
The escalating complexity of modern computing frameworks has resulted in a
surge in the cybersecurity vulnerabilities reported to the National
Vulnerability Database (NVD) by practitioners. Despite the fact that the
stature of NVD is one of the most significant databases for the latest insights
into vulnerabilities, extracting meaningful trends from such a large amount of
unstructured data is still challenging without the application of suitable
technological methodologies. Previous efforts have mostly concentrated on
software vulnerabilities; however, a holistic strategy incorporates approaches
for mitigating vulnerabilities, score prediction, and a knowledge-generating
system that may extract relevant insights from the Common Weakness Enumeration
(CWE) and Common Vulnerability Exchange (CVE) databases is notably absent. As
the number of hardware attacks on Internet of Things (IoT) devices continues to
rapidly increase, we present the Hardware Vulnerability to Weakness Mapping
(HW-V2W-Map) Framework, which is a Machine Learning (ML) framework focusing on
hardware vulnerabilities and IoT security. The architecture that we have
proposed incorporates an Ontology-driven Storytelling framework, which
automates the process of updating the ontology in order to recognize patterns
and evolution of vulnerabilities over time and provides approaches for
mitigating the vulnerabilities. The repercussions of vulnerabilities can be
mitigated as a result of this, and conversely, future exposures can be
predicted and prevented. Furthermore, our proposed framework utilized
Generative Pre-trained Transformer (GPT) Large Language Models (LLMs) to
provide mitigation suggestions.