## 2 Related Work
The first step in developing the security framework was to apply common MLTs, including NBs (naive Bayes), LRs (logistic regressions), SGDs (stochastic gradient descents), KNNs (K-nearest neighbors), SVMs (support vector machines), DTs, RFs (random forests), adaptive boosting, extreme gradient boosting, and LD \(\mid\) I As (linear discriminant analyses). Subsequently, a security architecture based on ANNs (artificial neural networks) has also been included, which takes into account numerous hidden layers. A new approach iReTADS was proposed in [24] to increase network security, while reducing network traffic by leveraging a potent real-time neural network for data summarization. Although data summarizing is a crucial part of data mining, there are no reliable ways to evaluate the summary at the moment. The goal of [PERSON] and [PERSON]'s study in [25] was to explore the challenges, drawbacks, and benefits of the proposed approaches by examining and thoroughly analyzing cybersecurity developments. [PERSON] et al. [26] described two-stage attack detection and attribution models which they developed for CPS, and specifically for ICS (industrial control systems). First, DTs were integrated with new Ensemble Deep Representation Learning models to detect attacks in unbalanced scenarios of ICS. In the next phase, Ensemble Deep Neural Networks (DNNs) were used to charac terize attacks. The proposed model was tested on practical datasets of gas pipelines and water treatment systems. [PERSON] et al. [27] proposed a technique for detecting attacks on cyber-physical systems. In their work, KNNs, DTs, bootstrap aggregations or bagging, and RFs were studied as classification models. In their proposed study, an auto-associative kernel regression model was used to improve the timely detection of attacks. Although the proposals were accurate, due to technical issues, their results were not sufficient. To predict attacks in virtual networks, [PERSON] et al. [28] developed Bayesian game theory IDS to prevent and predict future activities of monitored vehicles. [PERSON] et al. [29] proposed attack detection modules relying on Hilbert-Huang transforms and DLTs to detect attacks on DCs (direct current networks), MGs (microgrids), and DGs (distributed power generations). Their KHOs (Krill Swarm Optimizations) were a DLT for current election groups. DTs can play an important role in the development of IDS; however, these systems must handle large amounts of network traffic with multidimensional data, while being resilient and effective, and also reducing computational complexity with increased accuracy in their detection processes. The African Buffalo Optimal Decision Tree (ABODT) is an algorithm proposed by [PERSON], [PERSON], and [PERSON] [30] that uses the intelligent and social behavior of African buffalo to generate globally optimized decision trees. In order to use the African buffalo optimization (ABO) method as an optimizer to change the weights of the probabilistic neural network, [PERSON], [PERSON] et al. [31] suggested a hybridization strategy (PNN). It is critical to limit the number of input variables so as to reduce the computational cost of modeling and improve model performance in certain cases. A decision tree is a classification technique that can help in knowledge extraction from a database. When there are more features and instances, databases grow tremendously quickly and accumulate much data. Although decision trees have several issues, their key drawbacks are instability, local judgments, and overfitting for this enormous amount of data.