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| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2312.13530 |
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| _version_ | 1866908389868568576 |
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| author | Lin, Yu-Zheng Mamun, Muntasir Chowdhury, Muhtasim Alam Cai, Shuyu Zhu, Mingyu Latibari, Banafsheh Saber Gubbi, Kevin Immanuel Bavarsad, Najmeh Nazari Caputo, Arjun Sasan, Avesta Homayoun, Houman Rafatirad, Setareh Satam, Pratik Salehi, Soheil |
| author_facet | Lin, Yu-Zheng Mamun, Muntasir Chowdhury, Muhtasim Alam Cai, Shuyu Zhu, Mingyu Latibari, Banafsheh Saber Gubbi, Kevin Immanuel Bavarsad, Najmeh Nazari Caputo, Arjun Sasan, Avesta Homayoun, Houman Rafatirad, Setareh Satam, Pratik Salehi, Soheil |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_13530 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion Lin, Yu-Zheng Mamun, Muntasir Chowdhury, Muhtasim Alam Cai, Shuyu Zhu, Mingyu Latibari, Banafsheh Saber Gubbi, Kevin Immanuel Bavarsad, Najmeh Nazari Caputo, Arjun Sasan, Avesta Homayoun, Houman Rafatirad, Setareh Satam, Pratik Salehi, Soheil Cryptography and Security Artificial Intelligence Machine Learning 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. |
| title | HW-V2W-Map: Hardware Vulnerability to Weakness Mapping Framework for Root Cause Analysis with GPT-assisted Mitigation Suggestion |
| topic | Cryptography and Security Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.13530 |