## 3 Results
### Ontology and Knowledge Graph Construction
[PERSON] et al. [28] focused on the construction of a KG based on kernel audit logs, aiming to efficiently organize large amounts of data through the use of graph databases such as Neo4j. The KG is used for enabling semantic queries in threat hunting activities, which involves two stages. In the first stage, the hunter formulates hypotheses from different semantic levels based on previous IoC and identified patterns. These hypotheses are later evaluated through graph query and visualization provided by Neo4j in order to assist the hunter in the elimination of false positives. The performance of the threat hunter KG was evaluated by assessing the time cost, required space occupation, and query delay. The works related to the construction of ontologies or KGs in 2022 are summarized in Table 3.  
A unified ontology knowledge base for cybersecurity was constructed by [PERSON] et al. [62]. The ontology helps organizations to identify potential threats and design effective defensive methods. SPARQL queries allow for the extraction of relevant concepts for meaningful inference results, potentially leading to rapid responses when addressing a cybersecurity incident. [PERSON] et al. [63] presented a security incident remediation strategy targeting cloud, edge, and on-premises environments for protection of SMEs. The authors introduced an ontology structure for the description of properties and relationships, which is necessary for selecting appropriate security measures. A mapping functio \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Year Published** & **Brief Summary** & **Domain** \\ \hline
[52] & 2022 & Proposed a cloud-native ontology capable of connecting security-related data from different cloud sources to enhance CTI. & Cloud-native cyber incident response \\ \hline
[53] & 2022 & Proposed a CTI ontology that enables the automation and analysis of the available threat intelligence. & CTI \\ \hline
[54] & 2022 & Developed a generic ontology for the representation of IoT objects to encapsulate vulnerabilities, attack attribution, impact evaluation and mitigation strategies within a smart home environment. & Risk Assessment in IoT Environments \\ \hline
[26] & 2022 & Proposed an ontology for risk assessment modeling. & Security Risk Information \\ \hline
[56] & 2022 & Proposed an ontology and an automated information extraction method, capable of integrating the parsed information from CTI reports into each instance. & CTI \\ \hline
[57] & 2022 & Proposed AttackKG, a KG used for the automated extraction of attack behavior graphs from CTI reports. & CTI \\ \hline
[60] & 2022 & Proposed TINKER, a framework that utilizes ontologies and KGs for capturing cyber threat information. & CTI \\ \hline
[61] & 2022 & Supported the use of graph databases for near real time network log monitoring. & Network Log Files \\ \hline
[28] & 2022 & Presented a KG based on kernel audit logs for the efficient organization of big data, enabling querying during threat hunting activities. & Cyber Threat Hunting \\ \hline \end{tabular}
\end{table} 
Table 3: Works relevant to ontology or KG construction published in 2022. Records are arranged in an ascending alphabetical order. ontology, aiming to identify the security mechanisms required in each case by leveraging relevant cyber threat knowledge. To address the issue of blind SQL injection attacks against web application databases, [PERSON] et al. [64] constructed OBSQL, an ontology for blind SQL weaknesses detection capable of providing relevant threat mitigation techniques. In their work, the authors additionally discussed the impact of blind SQL injections, different attack scenarios, detection and exploitation of vulnerabilities, and the usefulness of ontologies within the cybersecurity field. [PERSON] et al. [65] presented an ontology designed for real time risk management within a secured environment by organizing data on anomalies and cyber threats and applying established methodologies [66].