# Intelligent Network Intrusion Prevention Feature Collection and Classification Algorithms
## 3 The Architecture of System ISTID
### Pre-Processing Normalized Data
Forms weight population Step 7--Crossover with the next community for every community of weight Step 8--Again, add exercise Step 9--When \(fitness\ value> threshold\), \(Mutation\) ulate performance Else Step 10--Form policy based on conduct Genetic Fuzzy Cognitive Temporal Adaptive Algorithm

This research has established and applied the algorithm of classification of the intelligent temporal adaptive genetic fuzzy. A modern exercise feature has been used for this reason, which helps to make decisions. In the case of instant comparison and period compare procedures, time constraints are often added. Finally, in this algorithm, a triangular membership function based on fuzzy logic has been applied for flushing and deflation. The essential advantage of the proposed algorithm is the usage of time limits, dynamic logic, and genetic algorithms as an efficient decision-making tool. In this model, two datasets were used to check the suggested IDS: the KDD cup dataset and the network trace dataset. The dataset module for the KDD cup consists of the data from the MIT Lincoln laboratory. This data set is a benchmark data set that is accepted in standard data sets to test intrusion detection systems. During this research study, the cup

Figure 3: Function with Given Parameter Triangular Membership. data collection for the KDD 1999 is used for the testing of the proposed algorithms because it is more widely used for the measurement of algorithms for intrusion detection. This dataset contains 41 attributes, and each is useful to explain the network behavior. These attributes have 38 numerical characteristics as well as 3 typical characteristics that give a total of 41. A one hour-long simulation of 15 min to produce and track attacks took place in the actual networks. The trace data collection was collected. The KDD cup dataset is the initial check for all the algorithms proposed in this research, and they are checked with network trace knowledge. The intrusion detection rates of the proposed algorithms are identical when they are performed with this dataset and with actual network trace datasets in low mobility and medium-mobility scenarios in this work. ## 4 Result and Discussion

This section discusses data sets, performance measurements, and experimental findings. The reasons why the proposed system is being achieved are presented here. ### Performance Analysis

In this study, the precision of the identification of intrusion was developed using the following measures to test the device proposed. Since the detection exactness is affected these metrics, recall, and G-measure are as follows:

### Precision

Precision is specified as the data ratio conditioned to the data related to the detection. The calculation is computed in Equation (6). \[Precision=\frac{PT}{PT+PF} \tag{6}\]

### Recall

The partnership with data necessary for the practical identification of data. The formula can be computed in Equation (7). \[Recall=\frac{PT}{PT+NP} \tag{7}\]

### G-Measure

The G-measure is used to be the fraction of accuracy minus reminder. It has a beta value which is typically 1:

\[G-Measure=\frac{\left(1+\alpha^{2}\right)\times Precision\times Recall}{\alpha \left(Precision+Recall\right)} \tag{8}\]

In Equation (8), the performance evaluation is based on the number, time, accuracy, and false-positive rates of the generated detectors (rules). The ISTID test dataset is used during each repeated improvement iteration. The accuracy of the rating and the false positive rate (FPR) is calculated as follows:

\[Accuracy=\frac{PT+NT}{PT+PF+NF+NT} \tag{9}\]

Equation (9), where true positive (\(PT\)) samples are classified as naturally valid, and false positive (\(PF\)) samples are wrongly classified as an irregular, real negative (\(NT\)) samples are appropriately classified as a different sample and false negative (\(NF\)). The tests are conducted utilizing the new method with the same data set, and the actual framework is shown in Table 1. Such experiments were conducted to identify attacks from Study, DoS, S2M or V2S.