# Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
## 3 The Architecture of System ISTID
### Pre-Processing Normalized Data  
On every record, the input data is standardized splitting a maximum value into the maximum value, and therefore all data in a single format with a range of values from zero to one. This preparation assists in making easier comparisons in the decision-making process. A new pre-processing algorithm is suggested for this reason, which is named the normalized intelligent GA-based techniques and intrusion detection (ISTID) algorithm. Furthermore, a more pre-processing algorithm called ISTID in these research papers is suggested. This is called the intelligent agent-based incremental sorting algorithm. These two methods for pre-processing are often used to pick attributes from a dataset. Fitness is an analytical tool used to determine the right benefit. applying the following formula, the fitness function is calculated:  
\[g_{j}\Big{(}B_{k},\ E_{k}^{b}\Big{)}=\left\{ \begin{array}{ll}\frac{1}{b+1}& \quad if\ B_{k}=E_{k}\\ \frac{1}{b}&\quad Otherwise\end{array} \right. \tag{2}\]  
Equation (2) where \(B_{k}\)--is a rule precedent and \(E_{k}\)--is the database fact. Each record is rotated here such that once the first, twice the second and so on, the first record is rotated. Now, due to migration rotation, the chromosomes are arranged differently. A crossover process takes place between two parent chromosomes to receive a new chromosome. A new child allele is selected depending on the fusion rate C r. the parent chromosomes are chosen. The fitness feature is added to the child chromosome after a new chromosome is produced. The crossover rate calculation formula is as follows:  
\[Rate\ of\ Crossover\ (D_{s})=\frac{Gene\ crossing\ number\ Number}{Length\ of\ the\ chromosome} \tag{3}\]  
Equation (3), the mutation rate \(N_{s}\)--is determined based on the number of mutations per database \(N_{Q}\)--is the length of the chromosome where \(O_{M}\)--is the mutation point. The mutation risk estimation rule is:  
\[rate\ of\ Mutation\ (N_{s})=\frac{N_{Q}}{O_{M}} \tag{4}\]In this analysis, the triangular membership feature was chosen to pass the input data to the fused label, as shown in Figure 2. The method for the estimation of membership values is therefore seen below in Equation (4):  
\[g(y)=\left\{ \begin{array}{ll}0&\quad if\ y\ \leq b\\ \frac{y-b}{c-y}&\quad if\ b\ \leq y\ \leq c\\ \frac{d-y}{d-c}&\quad if\ c\ \leq y\ \leq d\\ 0&\quad if\ y\ \geq d\\ \end{array} \right. \tag{5}\]  
In this scenario, the codes for the kind of attacks are low, medium, and large. This research was carried out for three-way participation as shown in Figure 3. The basis of knowledge consists of rules generated from the genetic and fugitive operation and trained using the proposed algorithm of classification, which is subject to time constraints. The rules are IF... BEFORE declarations that are compared internally and overlapping members. Membership features depict a fuzzy set graphically. The x-axis is the discourse universe, and the y-axis shows membership levels inside the [0, 1] interval. To create membership functions, simple functions are utilized. The membership function of a fluid set is to generalize the indicators for traditional sets in mathematics. It is an extension of appraisal, in fu  
Figure 2: Estimation of Membership. This method is incorporated with the suggested classification model, named the intelligent sequential adaptive genetic techniques and intrusion detection (ISTID)) described in Algorithm 1. The technique is used and the full phases of the algorithm proposed are as follows:  
``` Input: Dataset \(E_{j}=\{S_{1},\ S_{2},\ldots S_{n}\}\) Output: Groups of Classifications Step 1--Choose the first chromosomes. Step 2--Submit fuzzy chromosome membership function Step 3--Assess the fitness of chromosomes Step 4--Using threshold values to pick a new population Step 5--Apply time restrictions Step 6--While the population is present i. Compare the process immediately ii. Place the ranking comparing values instantaneously iii. Comparison of performance interval iv.