Ontology-Driven Framework for Trend Analysis of Vulnerabilities and Impacts in IoT Hardware

被引：3
|
作者：
Bandi, Charan [2 ]
Salehi, Soheil [1 ]
Hassan, Rakibul [2 ]
Manoj, Sai P. D. [2 ]
Homayoun, Houman [1 ]
Rafatirad, Setareh [2 ]
机构：
[1] Univ Calif Davis, Sch Elect & Comp Engn, Davis, CA 95616 USA
[2] George Mason Univ, Sch Elect & Comp Engn, Fairfax, VA 22030 USA
来源：
2021 IEEE 15TH INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2021) | 2021年
基金：
美国国家科学基金会;
关键词：
National Vulnerability Database (NVD); Common Vulnerability and Exposure (CVE); Common Weakness Enumeration (CWE); Internet of Things (IoT); Hardware Vulnerability; Ontology Learning; Natural Language Processing (NLP);
D O I：
10.1109/ICSC50631.2021.00045
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The number of publicly known cyber-security vulnerabilities submitted to the National Vulnerability Database (NVD) has increased significantly. However, it is cumbersome to explore useful information from this large corpus of unstructured data to find meaningful trends over time without proper tools. Prior works with this purpose have mainly focused on the software vulnerabilities and fail to provide a storytelling framework that can extract useful information about the relationship and trends within the CVE and CWE databases over time. Additionally, hardware attacks on IoT devices are evolving very rapidly due to the recent proliferation of computing devices in mobile and IoT domains. Thus, herein, we focus on IoT hardware vulnerabilities and develop an Ontology-driven Storytelling Framework (OSF) which aims to identify similar patterns of vulnerabilities over time, to help mitigate the impacts of vulnerabilities or predict and prevent future vulnerabilities.
引用
收藏
页码：211 / 214
页数：4
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