## 4 Analysis and Results  
Based on the methodology outlined in Section 3, we extracted and interpreted themes related to cybersecurity in the automotive sector. Table 3 identifies 20 different topics from all the articles published on the topic of the maintenance of cybersecurity in the automotive sector 20. Table 4 (main text) shows the extracted topics together with the topic label we have assigned to it and the most common terms. For each paper, our analysis also includes a score of topic loading. The results are analyzed in a multi-step process. Initially, topic modeling assigns probabilities to words within topics and topics within documents. Tables 5 and 6 present summary statistics of the loadings for each topic, indicating the mean loading across all articles, as well as the lowest and highest loading observed within a specific topic. In this context, \"loading\" refers to the probability distribution of words in a topic or the distribution of topics in a document. Thus, we can observe from Table 6 that topics with an average loading in journals are Topic 3, focusing on the cost of cybersecurity (0.248); Topic 9, addressing the vehicle's ability to integrate new cybersecurity features (0.163); and Topics 6 and 8, specifying the capability of dealers to maintain cybersecurity systems (0.105) and the increase in vehicle interconnectivity (0.105). The remaining topics exhibit lower average loadings. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Topic Number** & **Topic Label** & **Most Common Words** \\ \hline Topic 1 & Diversity and intensity of attacks. & Attacks, attack, zero-day, vulnerability, CAN, data, smart, detection, CAN Bus, autonomous, connectivity, lack, architecture, techniques, infotainment, countermeasures, pretesting, cyber, information, vulnerabilities. \\ \hline Topic 2 & Vehicle Security requirements & Requirements, automotive, security, embedded, cybersecurity, use, collaborative, lifecycle, standards, suppliers, architecture, communication smart, define, application, approach, assembly, future, on board, limitations. \\ \hline Topic 3 & Costs of cybersecurity technology. & Costs, capacity, design, analysis, driving, cybersecurity, challenges, system, development, autonomous, supplier, electric, connected, used, product, automotive, embedded, application, customer, ICE. \\ \hline Topic 4 & Collaborations in the supply chain. & Suppliers, heterogeneity, cybersecurity, automotive, lifecycle, collaboration, processes, requirements, model, management, customer, practices, ASPICE, design, manufacturer, updates, RACI, challenges, attack, threats. \\ \hline Topic 5 & Heterogeneity of devices, components and vehicle variants. & Vehicles, autonomous, data, industry, technology, model, heterogeneity; software, sensors, ECUs, smart, connectivity, cybersecurity, future, blockchain, ADAS, supplier, aftermarket, camera, wifi, network. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: List of topics with labels and the most frequent terms per topic. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Word** & **Frequency** \\ \hline
1 & attacks & 0.962729 \\
2 & components & 0.936545 \\
3 & requirements & 0.935215 \\
4 & standards & 0.930916 \\
5 & devices & 0.861852 \\
6 & connectivity & 0.693456 \\
7 & supply & 0.580769 \\
8 & capacity & 0.456185 \\
9 & chain & 0.373364 \\
10 & heterogeneity & 0.358709 \\
11 & suppliers & 0.253452 \\
12 & costs & 0.241683 \\
13 & lifecycle & 0.095172 \\
14 & security & 0.027945 \\
15 & systems & 0.024527 \\
16 & challenges & 0.014775 \\
17 & data & 0.013632 \\
18 & system & 0.012949 \\
19 & cyber & 0.012484 \\
20 & CAN * & 0.011906 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: List of most frequent words in the corpus.