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.

现代计算框架的复杂性不断增加，导致从业人员向国家漏洞数据库（NVD）报告的网络安全漏洞激增。尽管 NVD 具有提供最新洞察的重要性，但在没有适用的技术方法的情况下，从如此大量的非结构化数据中提取有意义的趋势仍然具有挑战性。该研究提出了一种综合策略，包括解决漏洞、分数预测和从 CWE 和 CVE 数据库中提取相关洞察的知识生成系统。为了应对物联网设备的硬件攻击不断增长的问题，作者提出了基于机器学习的硬件漏洞到弱点映射（HW-V2W-Map）框架，该框架使用本体驱动叙事框架自动更新本体以识别漏洞随时间的演变趋势，并提供解决漏洞的方法。该框架还利用生成式预训练转换器（GPT）大型语言模型提供解决方案建议。

硬件弱点脆弱性映射框架 HW-V2W-Map: GPT 辅助缓解建议的根本原因分析

HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for  Root Cause Analysis with GPT-assisted Mitigation Suggestion

Current test and evaluation (T&E) methods for assessing machine learning (ML)
system performance often rely on incomplete metrics. Testing is additionally
often siloed from the other phases of the ML system lifecycle. Research
investigating cross-domain approaches to ML T&E is needed to drive the state of
the art forward and to build an Artificial Intelligence (AI) engineering
discipline. This paper advocates for a robust, integrated approach to testing
by outlining six key questions for guiding a holistic T&E strategy.

本文主张通过指导整体的测试与评估策略的六个关键问题来推动跨领域机器学习测试与评估方法的发展，从而建立人工智能工程学科。