# Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
## 3 The Architecture of System ISTID
### Pre-Processing Normalized Data
Table 1, with respect to precision, recall, and G-measure,shows the results obtained for all four different types of attacks. Although the ISTID utilizes the same collection of data as the current IGS, the exact classification with the usage of intelligent agents and the time restrictions are outlined in Table 1. A technically adaptive genetic fugitive method is used. In Table 1, the precision, reminder, and F-measurement of the data are improved when the proposed ISTID is classified. That is because ISTID utilizes rational agents and time constraints to make judgments. The system proposed is compared with the existing ISTID (and decision tree classification methods). It has been observed from the experiments carried out that ITAGFS is better able to detect accuracy than the other two techniques. The main reason for this improvement is that the ISTID proposed not only insists on the use and use of the smart agents as well as the knowledge basis to make effective decisions on genetic operations and fugitive time regulations. The efficiency review is demonstrated five separate tests for the identification of four attack types: Probe, DoS, S2M and V2S. The suggested method of intrusion detection is conducted. The detection precision may be observed for the identification of four forms of attacks in the different experiments. However, the precise identification of DoS attacks in both tests is higher than the precision of the identification of certain forms of aggression. The false-positive intravenous identification method and current classifiers study as seen in Figure 4.

The precision of measurement of the device suggested is determined using Equation (5). The study of precision in the identification of attack forms Review, Probe, DoS, S2M and V2S on the proposed method and the current classifiers. It is noted that ISTID is \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attacks** & **Precision** & **Recall (\%)** & **G-Measure (\%)** & **Performance Analysis (\%)** \\ \hline DoS & 88.62 & 92.31 & 91.25 & 92.35 \\ S2M & 92.65 & 94.62 & 93.52 & 94.52 \\ Probe & 94.52 & 92.16 & 96.14 & 93.64 \\ V2S & 92.36 & 96.72 & 94.25 & 97.25 \\ ISTID & 99.14 & 98.36 & 99.26 & 98.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of Attacks. Figure 4: Number of Attacks vs. Precision (%). much better than Probe and other classifiers' performance. Compared to this existing Probe directional system and the different decision-making bodies such as D 6.3 and Enhanced D5.6 the most impressive part of ISTID is a significant improvement. There are several explanations of why ISTID has improved detection precision. Second, only chosen features are included, as shown in Figure 5. Third, it utilizes transient and pointless laws shot into successful decision-making rational agents. Third, for optimized rules, genetic operations are used. Finally, it calls temporary constraints in the implementation of logic. The efficiency of the proposed program is higher as opposed to current systems can be seen in Figures 5 and 6. The use of most essential features selected the feature selection algorithm, which uses intelligent agents for decision making, enhances the accuracy of the classification process leading to an increase of intrusion detection using temporal constraints, bizarre rules and genetic algorithms. Table 2 shows an optimum set of functionality chosen the proposed function selection algorithms called the Min-Max Function Selection ISTID Normalized Intelligent GA Algorithm. It is noted in Table 2 that only 14 features from a collection of 32 features present in the KDD cup dataset have been picked from the suggested uniform Intelligent Genetic Fuzzy Cognitive Temporal Adaptive feature selection algorithm. It was evident that as compared to the current frameworks, it increases the efficiency of the proposed ISTID Algorithm.