## 3 Databases Creation
### Unique Source of Information  
The database is a unique source of information on vulnerabilities and exploits because it has a lot of information that cannot be found in any other database due to the fact that the data are collected from various sources, aggregated, and correlated. Since many vulnerabilities and exploits affecting IoT devices have never been catalogued in databases, it is necessary to review additional sources to maintain awareness of IoT threats. In addition to the data collected from the structured sources listed in Table 1, we also looked for recent posts and articles on the Internet. Blogs are a good source but are also difficult to follow and extract relevant information from. They are written by individual researchers, hacking groups, or security companies that showcase their achievements in terms of hacking smart devices. Since they are rarely devoted exclusively to this topic, individual IoT-related posts must be filtered. The relevant metadata can be extracted from the raw text for these types of sources. One other unique feature of the VARIoT vulnerability database is the correlation and aggregation of vulnerability information from various publicly available sources. As mentioned earlier, the information is taken both from structured vulnerability databases and from blogs and individual entries, so we have more information available. The enrichment and selection of the most relevant information makes the entries more complete and valuable than a single entry. Data sources are fluid in nature - they often change, new ones arrive, and old ones are no longer developed or updated. Some of the sources we previously used are no longer available on the Internet: BID, ExploitDatabase.net, VUL-HUB, and IVD. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Short Name** & **Long Name** & **Reference** \\ \hline BID & SecurityFocus Bugtraq & [20]—not active \\ CERT CC & Carnegie Mellon University CERT Coordination Center & [21] \\ CISA KEY & Known Exploited Vulnerabilities Catalog & [22] \\ CNNVD & Chinese National Database of Information Security & [23] \\ CNVD & China National Vulnerability Database & [24] \\ ExploitDatabase & EXPLOIT-DATABASE.NET & [25]—not active \\ Exploit-DB & Exploit Database by Offensive Security & [26] \\ ICS-CERT CN & Chinese ICS-CERT website & [27] \\ IVD & ICS Vulnerability Database & [28]—not active \\ JVNDB & Japan Vulnerabilities Notes Database & [29] \\ NVD & National Vulnerability Database & [30] \\ Packet Storm & Packet Storm Security & [31] \\ Vulmon & Vulmon Vulnerability Search Engine & [32] \\ VUL-HUB & VUL-HUB Information Security Vulnerability Portal & [33]—not active \\ ZDI & Zero Day Initiative & [34] \\ ZSL & Zero Science Lab & [35] \\ VARIoT & Vulnerability and Attack Repository for IoT database & [7] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sources of information. Figure 2: 100 most popular vendors in the devices database. The inclusion of a wide range of structured and reliable information sources has necessitated the use of various mechanisms for information management, automatic correlation, detection of semantically consistent fragments, and advanced mechanisms for managing trust in both the information source and the information itself. In addition to the information found in structured databases, a significant amount of data on IoT device vulnerabilities and exploits can be found in other unstructured sources, such as websites, reports, and posts on various specialized blogs. The main challenge with unstructured sources is the problem of extracting specific information; this applies in particular to the types of vulnerabilities, their criticality, and the models and manufacturers of vulnerable devices. To solve these problems, we used ML and natural language processing (NLP) methods appropriately adapted to these specific needs.