# A New AI-Based Semantic Cyber Intelligence Agent
## 4 Results
As seen from Figure 5, technology building blocks, such as Microsoft SQL Server, Microsoft Dataverse, Microsoft Power Automate, and Microsoft Power BI, were used to generate AI-based cyber intelligence from both Twitter data and Kaspersky's attack statistics (i.e., [[https://statistics.securelist.com/](https://statistics.securelist.com/)]([https://statistics.securelist.com/](https://statistics.securelist.com/))) (accessed on 3 March 2023). Table 6 presents comprehensive information on the Twitter data collected over a 7-month period. It includes the time frame, number of tweets, unique users, unique locations, unique languages, total retweets, average confidence scores for negative, neutral, and positive sentiment analyses, and the number of translated tweets. The table reveals an increase in the quantity of tweets and users over time, while the number of languages remained stable. Retweet counts varied each month, with November 2022 having the highest total. The average confidence scores for sentiment analyses remained stable, with negative sentiment showing higher confidence than neutral and positive sentiment. Only a small portion of tweets were translated, with December 2022 having the highest percentage. Figure 6 displays average daily sentiment score fluctuations on time-series data as produced by the cognitive service agent. Figure 7 focuses solely on average monthly negative sentiments for Russia, China, Australia, Ukraine, India, and Iran. As highlighted in Figure 7, the monthly average of negative sentiment for Russia was at its peak in January 2023 with an average negative sentiment confidence of 0.74. Negative sentiment cyber-related posts are considered alerts [16]. These alerts or negative sentiments produced by the cognitive service agent were used to generate the psychological effect (i.e., number 5 semantic output of the aggregation agent shown in Table 1) of the cyber threat dimension. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Time**} & **No. of** & **No. of** & **No. of** & **No. of** & **Total** & **Avg.** & **Avg.** & **Avg.** \\  & **Twitters** & **Users** & **Locations** & **Languages** & **Retweets** & **Confidence of** & **Confidence of** & **Confidence** \\  & & & & & **of – [PERSON]** & **Neut. Seti.** & **of + Ve [PERSON]** & **Translations** \\ \hline October 2022 & 3954 & 3556 & 1588 & 38 & 3,727,756 & 0.36 & 0.43 & 0.21 & 941 \\ November 2022 & 6470 & 5875 & 2358 & 38 & 9,981,856 & 0.34 & 0.43 & 0.23 & 1283 \\ December 2022 & 6512 & 8544 & 2225 & 42 & 7,565,946 & 0.35 & 0.42 & 0.23 & 1533 \\ January 2023 & 6685 & 5785 & 2364 & 40 & 7,802,301 & 0.36 & 0.40 & 0.24 & 1419 \\ February 2023 & 5976 & 5053 & 2114 & 43 & 4,276,479 & 0.37 & 0.42 & 0.21 & 1373 \\ March 2023 & 6634 & 5749 & 2357 & 41 & 4,799,540 & 0.36 & 0.43 & 0.21 & 1469 \\ April 2023 & 1155 & 1083 & 58 & 27 & 713,083 & 0.40 & 0.41 & 0.20 & 258 \\ Total & 37,386 & 30,706 & 10,178 & 54 & 38,866,961 & 0.36 & 0.42 & 0.22 & 8199 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Processing of Tweets for AI-based Cyber-Threat Intelligence. Figure 6: Daily average of tweet sentiments (8 November 2022 recorded the highest level of negative sentiment). The CNN-based anomaly detection algorithm identified 7 anomalies for Russia, 5 anomalies for India, 5 anomalies for Iran, 4 anomalies for Ukraine, 4 anomalies for China, and finally, 12 anomalies for Australia, as depicted in Figure 8. These anomalies were identified by the social media agent by analysing live tweets during the monitored period. It should be mentioned that the number of tweets for each of the countries varied every month depending on the number of cyber-related issues and how the general public reacted psychologically to those issues. Figure 9 demonstrates the ranking of the number of tweets for each of the monitored countries between 13 October 2022 and 6 April 2023. As seen from Figure 9, in January  
Figure 8: Anomaly detection detected cyber anomalies in Russia, China, Australia, Ukraine, India, and Iran.