# A New AI-Based Semantic Cyber Intelligence Agent
## 2 Background and Literature Review
### NLP-Based Cyber Intelligence from Social Media  
Analysing cyber-related social media posts on Twitter began nearly a decade ago [31], but these early studies did not leverage the potential of machine learning (ML) and deep learning (DL) techniques for automated critical analysis. Instead, they relied on the manual application of general statistical techniques to gain insights into cyberbullying and related issues. In [17], a methodology utilizing sentiment analysis of tweets is described for predicting cyber-attacks. The authors employed machine learning algorithms to categorize tweets as security-related, positive, or neutral and then examined the relationship between sentiment scores and cyber-attacks reported by Google News using a regularised regression model. Their findings suggest that this methodology can serve as a warning system for identifying potential cyber-attacks, particularly when the coefficient of determination is high. They also discuss the response of hacktivists to candidates' comments and actions in the 2016 US presidential elections, providing examples. Ref. [21] utilizes the term frequency-inverse document frequency (TF-IDF) to extract features from a dataset of 2000 tweets, comparing the performance of five classifiers and determining that logistic regression is the best classifier for detecting bullying tweets. In [22], TF-IDF and LGBM algorithms are employed to identify cyber-attacks in darknet traffic, achieving a high accuracy of 98.97% compared to other algorithms. Ref. [19] employs two machine learning-based classifiers to analyse a large-scale Twitter dataset, revealing negative sentiment across various themes. Ref. [20] identifies sensitive keywords that could lead to vulnerability when combined with benchmarked cyber keywords using LDA, while [23] analyses COVID-19 misinformation spread on Twitter through sentiment, emotion, topic, and user attributes, highlighting the denial of the pandemic and dissemination of false information. Table 3 showcases the diverse NLP algorithms used in these studies, with the proposed system presenting the most comprehensive use of NLP-based algorithms for generating seven-dimensional cyber intelligence.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategic Questions Answered** & **Dimension of Cyber Threat** & **Reference** \\ \hline
1. What type of threat? & Threat spectrum (e.g., malware, spyware) & [24; 27; 29; 30] \\
2. Who is attacking? & & \\
3. Where is the attack coming from? & & \\
4. Why is the attack happening? & & \\
5. What is the motivation for this attack? & & \\
6. Who is the target? & & \\
7. Who is the victim of the cyber-attack? & & \\
8. What are the major cyber-related concerns? & & \\
9. What is the impact? & & \\
10. What is the societal perception? & & \\
11. How do cyber-attacks affect society? & & \\
12. How much negativity is generated at a psychological level? & & \\
13. What is the severity level of the threat? & & \\
14. What is the intensity of the cyber threat? & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strategic questions answered by multi-dimensional cyber threat intelligence.