HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion
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.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
LLM-HyPZ: Hardware Vulnerability Discovery using an LLM-Assisted Hybrid Platform for Zero-Shot Knowledge Extraction and Refinement
The rapid growth of hardware vulnerabilities has created an urgent need for systematic and scalable analysis methods. Unlike software flaws, which are often patchable post-deployment, hardware weaknesses remain embedded …
Evaluating Open-Weight LLMs for Generating Structured Threat Information for Autonomous Vehicle Vulnerabilities
Connected and Autonomous Vehicles (CAVs) rely on interconnected software and hardware components, including sensors, Electronic Control Units, in-vehicle infotainment systems, and telematics units, where vulnerabilities …
Autonomous VehiclesGeneralization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning
Code Pre-trained Models (CodePTMs) based vulnerability detection have achieved promising results over recent years. However, these models struggle to generalize as they typically learn superficial mapping from source cod…
Multi-Task LearningVulnerability DetectionQuantifying Security Vulnerabilities: A Metric-Driven Security Analysis of Gaps in Current AI Standards
As AI systems integrate into critical infrastructure, security gaps in AI compliance frameworks demand urgent attention. This paper audits and quantifies security risks in three major AI governance standards: NIST AI RMF…
Attack Techniques and Threat Identification for Vulnerabilities
Modern organizations struggle with insurmountable number of vulnerabilities that are discovered and reported by their network and application vulnerability scanners. Therefore, prioritization and focus become critical, t…
Management