## 8 Discussion and Potential Opportunities  
This discussion offers an in-depth comparison of six studies previously mentioned in the section focused on security content detection: SYNAPSE, DeepNN, DataFreq, CyberX [144], text mining, and SONAR. The SONAR model leverages cosine similarity for its predictions. Despite the common use of cosine similarity in text and vector space analysis, the model exhibits a relatively low precision of 23%. This suggests that while the model can identify true positives, it also generates a significant number of false positives. Consequently, SONAR might be better suited for applications where the primary goal is recall rather than precision, or where further filtering steps can be applied to reduce false positives. The text-mining [146] approach uses filtering techniques to achieve a precision of 84%. This high precision indicates that the model effectively minimizes false positives. Filtering techniques often involve rules or patterns to exclude irrelevant or erroneous data, making this model suitable for applications requiring high accuracy in positive predictions, such as specific keyword extraction or targeted information retrieval. The CyberX [144] model utilizes SWRL to achieve a precision of 86%. SWRL is a powerful tool for representing and reasoning with rules on the Semantic Web. The high precision rate indicates the model's effectiveness in applying semantic rules to filter and identify relevant information accurately. This makes CyberX particularly useful for scenarios requiring precise data extraction from large datasets, such as monitoring cyber threats or sentiment analysis. DataFreq [150] employs Logistic Regression to achieve a recall of 84%. Logistic regression is a statistical method commonly used for binary classification problems. A recall of 84% suggests that the model effectively identifies a large proportion of true positives. This makes it useful in applications where missing true positives is costly, such as in fraud detection or medical diagnosis. SYNAPSE [148] utilizes an SVM to achieve a recall of 90%. SVMs are known for their robustness in high-dimensional spaces and their effectiveness in classification tasks. A recall of 90% indicates the model's proficiency in capturing true positives, making it ideal for critical applications where it is essential to identify as many relevant instances as possible, such as in image recognition or bioinformatics. In summary, the SONAR [147] model achieved a precision of 23% using the cosine similarity algorithm. This low precision suggests that SONAR's effectiveness in identifying relevant instances was relatively limited compared to other models. In contrast, text-mining [146] model demonstrated a significantly higher precision of 84% through filtering techniques. This indicates a more refined ability to accurately classify relevant data. CyberX [144] achieved an impressive precision of 86% utilizing SWRL. This performance highlights its robust capability in precise classification. DataFreq [150]: Focusing on recall, DataFreq attained a recall rate of 84% with Logistic Regression (LR). While its precision was not measured, the recall rate suggests a strong ability to identify relevant instances. SYNAPSE [PERSON] et al. (2021) achieved a higher recall of 90% using Support Vector Machines (SVM). This indicates a superior capacity to retrieve relevant instances compared to DataFreq. DeepNN model reached an even higher recall of 94% through convolutional neural networks (CNN). This suggests an excellent performance in identifying relevant data points, Figure 11 presents the model's performance. By examining the total and average values of all factors combined and considering how the requirements and factors balance out based on their sum or average, we can see that DeepNN outperforms other techniques in both total and average figure of merit. While there are multiple techniques for char-based feature extraction available, we suggest taking a more semantic-based approach for this step.