# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 6 Discussion and Synthesis  
The four previously discussed vulnerability approaches use diverse techniques and algorithms with various inputs and outputs. These approaches have advantages and drawbacks and aim to reduce the high rate of false positives and negatives. However, their effectiveness needs further improvement. It is worth noting that the AI-based approach represents a trend in scientific research in vulnerability detection and cyber risk prediction. This is based on the analysis of the six methods (Table 7) and observations from the connected papers (Table 8). This fact is supported by the newly published methods and their promising results. Moreover, this category depends on generating reliable datasets, which is labor-intensive and time-consuming. In this context, we found that converting model inputs to vector form using various techniques is advisable, as detailed in Section 4. This involves preserving the input's lexical, syntaxial, and semantic states. Concerning ML models, Logistic Regression (LR), Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Deep Belief Network (DBN), Extra Trees Classifier (ETC), Voting Classifier (VC), Random Forest (RF), K Nearest Neighbor (KNN), Bagging Classifier (BC), Gradient Boosting (GB), AdaBoost Classifier (AC), XGBoost (XB), among others, are used alone or combined in many studies related to vulnerability detection solutions with various levels of accuracy and depending on the nature and the attack complexity. Hybrid ML solutions are mentioned in the literature review; we notice that ML techniques are combined with either a layer of statistical criteria, feature selection based on TFIDF, the Looking-Back concept, or other ML models. Regarding the DL models, several studies proposed attack detection solutionsincluding one or multiple models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Feedforward Neural Networks (FNN), Gated Recurrent Units (GRU), Variational Autoencoders (VAE), Graph Neural Networks (GNN), Autoencoders (AEs), Deep Belief Networks (DBNs), Generative Adversarial Networks (GANs), Deep Reinforcement Learning (DRL), among others. The DL can improve their performance if they are associated with another layer of Metaheuristic Algorithms (MAs) [158]. These MAs contribute to optimizing and tuning DL models and improving their effectiveness. This combination enhances the detection and response of cyberattack capabilities [159]. Moreover, many studies explored the advantages of AI/XAI in cyberattacks detection. Proposed studies including these features are referenced in \"connected papers\", the third column of Table 7. Finding the ideal AI model with accurate hyperparameters is still challenging and requires further study and real-world evaluation to reduce hallucination issues and errors. In terms of the feature model-based approach, the main objective is to utilize this method in order to detect vulnerabilities. This is performed by providing a comprehensive overview of the system components, which is presented in graphical and textual notations. Additionally, it allows for the current dependencies and potential correlations between all sub-elements of FM (CVE, product, RC, CPE, etc.) to be identified. This capability helps identify and analyze the target system in depth, diagnose possible security flaws, and mitigate cyber risks. For a more accurate FM, it is recommended to check the relevance of the security data source and the asset inventory. It remains crucial to note that large configuration variability increases FM complexity, requiring human interaction for maintenance, which is time-consuming and prone to errors.