## 3 Materials and Methods
### Distributed Multilayer Perceptron Classifier (MLPC) Architecture Development of the Proposed Model  
The attack percentages are handled using the architecture development model of the proposed system, called Distributed Multilayer Perceptron Classifier (MLPC). The MLPC uses all the available features from the dataset. According to an example, the simulation environment, which contains 15 vehicles, generates 75 features and is loaded into machine learning models. The initial layer of the MLPC architecture is the feature input. Therefore, the model attempts to identify the DDoS attack occurrence and generates the results in labeled Boolean. The final layer uses two values for the identification of an attack occurrence or nonoccurrence. The five layers of the model outperform the smaller architecture for the proper development of the architecture development. The testing of the dataset is performed using the numbers with a layered architecture. The testing performs better on the third and fourth layers of the NN. Algorithm 4 provides the running of the simulation with the high-performance architecture of the network.  
```
1 Input: All values from the network
2 Output: Prints \(F1\) score
3 Steps:
4 Start
5 for\(p\) from \(80\) to \(100\)do
6 for\(q\) from \(5\) to \(10\)do
7 for\(i\) from \(2\) to \(10\)do
8\(layer\leftarrow[p,\,q,\,k,\,76,\,2]\)
9\(design\_model(layer)\)
10 apply (Algorithm 5, \(train_{data},test_{data}\))
11\(fit\_model(training\_data)\)
12\(get\_predictions(testing\_data)\)
13\(determine()\gets F1\) Score
14\(if(f1>0.95)\)then
15\(print(F1\) Score, layers)
16 Endfor
17 Endfor
```  
**Algorithm 4**Layers determination using Brute Force Method  
Algorithm 4 collects the results and exports them to a CSV file. In this context, if the combination of layers works multiple times, it runs all the simulation environments. Through this, the developed model has a universal identity and effectiveness rather thanoperating on only a single attack scenario. Inside the proposed MLPC architecture, the highest values for median and mean over F1 score are [87; 9; 4, N, 2], where N is the number of features given through the dataset. Algorithm 4 it refers to the input layer, and 2 refers to the output layer of the model. From the dataset, we use all the feature-labeled columns as labels. The values 87, 9, and 4 show NN's hidden layers. In addition, 91.5% is the average F1 score, and 95.9% shows the median score over the network architecture. Algorithm 5 decides the training and testing dataset, splitting and fine-tuning the dataset according to the reference model. The training and testing dataset is then provided to Algorithm 4 for ML algorithm classification.  
```
1. Input: Complete Dataset
2. Output:\(train\)\(g_{data}\), \(test_{data}\)
3. Steps:
4. Start
5. \(Split(train\)\(g_{data}\), \(test_{data}\)).
6. \(train^{data}=Rand\_method\big{(}data^{complete}\), \(train\big{)}\)
7. \(test^{data}=Rand\_method\big{(}data^{complete}\), \(test\big{)}\)
8. \(Model()_{train}^{data}\)
9. \(model\_train\big{(}data^{test})\)
10. Repeat steps 4, and 5
11. \(Accuracy_{improve}=param()\)
12. \(1f(accuracy=satisf\,field())\)
13. \(data^{predict}=new_{data}\)
14. End
```  
**Algorithm 5**Decision about training and testing dataset