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model_dt.Rmd
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model_dt.Rmd
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```{r}
library(randomForest)
library(caret)
library(e1071)
library(rpart)
library(rpart.plot)
```
Building the model by using Decision tree
```{r}
# Decision Tree using 5 attrbutes and 1000 training tuples and 10000 testing tuples
decision_tree_model <- rpart(result ~ protocol_type+srv_rerror_rate+flag+rerror_rate+logged_in, data = training2[1:1000,], method = "class")
# Predicting:
decision_tree_pred <- predict(decision_tree_model, testing_data[1:10000,], type = "class")
decision_tree_pred
testing_data=training2[100000:125973,]
right <- decision_tree_pred==testing_data[1:10000,"result"]
answer<-table(decision_tree_pred,testing_data[1:10000,"result"])
answer
round(prop.table(answer,1)*100,2)
# Plot of the Decision Tree
rpart.plot(decision_tree_model, main = "Classification Tree",
extra = 102, under = TRUE, faclen = 0)
# Test results on our subTesting data set:
confusionMatrix(prediction1, subTesting$classe)
```
decision_tree_pred dos normal probe r2l u2r
dos 3310 242 91 5 0
normal 145 4936 130 76 3
probe 238 125 698 1 0
r2l 0 0 0 0 0
u2r 0 0 0 0 0
> round(prop.table(answer,1)*100,2)
decision_tree_pred dos normal probe r2l u2r
dos 90.73 6.63 2.49 0.14 0.00
normal 2.74 93.31 2.46 1.44 0.06
probe 22.41 11.77 65.73 0.09 0.00
r2l
u2r
```{r}
# Decision Tree using 5 attrbutes and 100000 training tuples and 25974 testing
decision_tree_model <- rpart(result ~ protocol_type+srv_rerror_rate+flag+rerror_rate+logged_in, data = training2[1:100000,], method = "class")
# Predicting:
decision_tree_pred <- predict(decision_tree_model, testing_data[1:25974,], type = "class")
decision_tree_pred
right <- decision_tree_pred==testing_data[1:25974,"result"]
answer<-table(decision_tree_pred,testing_data[1:25974,"result"])
answer
round(prop.table(answer,1)*100,2)
# Plot of the Decision Tree
rpart.plot(decision_tree_model, main = "Classification Tree",
extra = 102, under = TRUE, faclen = 0)
# Test results on our subTesting data set:
confusionMatrix(prediction1, subTesting$classe)
```
> answer
decision_tree_pred dos normal probe r2l u2r
dos 8484 636 200 14 0
normal 364 12925 357 190 9
probe 619 337 1836 3 0
r2l 0 0 0 0 0
u2r 0 0 0 0 0
> round(prop.table(answer,1)*100,2)
decision_tree_pred dos normal probe r2l u2r
dos 90.89 6.81 2.14 0.15 0.00
normal 2.63 93.36 2.58 1.37 0.07
probe 22.15 12.06 65.69 0.11 0.00
r2l
u2r
```{r}
# Decision Tree using 8 attrbutes and 1000 training tuples and 25974 testing
decision_tree_model <- rpart(result ~ protocol_type+srv_rerror_rate+flag+rerror_rate+logged_in+dst_host_rerror_rate+src_bytes+ serror_rate+rerror_rate, data = training2[1:100000,], method = "class")
# Predicting:
decision_tree_pred <- predict(decision_tree_model, testing_data[1:25974,], type = "class")
decision_tree_pred
right <- decision_tree_pred==testing_data[1:25974,"result"]
answer<-table(decision_tree_pred,testing_data[1:25974,"result"])
answer
round(prop.table(answer,1)*100,2)
# Plot of the Decision Tree
rpart.plot(decision_tree_model, main = "Classification Tree",
extra = 102, under = TRUE, faclen = 0)
# Test results on our subTesting data set:
confusionMatrix(prediction1, subTesting$classe)
```
round(prop.table(answer,1)*100,2)
decision_tree_pred dos normal probe r2l u2r
dos 93.69 5.44 0.87 0.00 0.00
normal 1.47 96.55 0.43 1.50 0.05
probe 0.00 16.49 83.17 0.26 0.07
r2l
u2r
```{r}
# Decision Tree using 8 attrbutes and 10000 training tuples and 25974 testing
decision_tree_model <- rpart(result ~ protocol_type+srv_rerror_rate+flag+rerror_rate+logged_in+dst_host_rerror_rate+src_bytes+ serror_rate+rerror_rate, data = training2[1:100000,], method = "class")
# Predicting:
decision_tree_pred <- predict(decision_tree_model, test.final, type = "class")
decision_tree_pred
right <- decision_tree_pred==test.final[,"result"]
answer<-table(decision_tree_pred,test.final[,"result"])
answer
round(prop.table(answer,1)*100,2)
# Plot of the Decision Tree
rpart.plot(decision_tree_model, main = "Classification Tree",
extra = 102, under = TRUE, faclen = 0)
# Test results on our subTesting data set:
confusionMatrix(prediction1, subTesting$classe)
```
answer
decision_tree_pred dos normal probe r2l u2r
dos 5151 82 108 187 0
normal 359 9226 1 1570 35
probe 231 402 997 442 2
r2l 0 0 0 0 0
u2r 0 0 0 0 0
`
round(prop.table(answer,1)*100,2)
decision_tree_pred dos normal probe r2l u2r
dos 93.18 1.48 1.95 3.38 0.00
normal 3.21 82.44 0.01 14.03 0.31
probe 11.14 19.38 48.07 21.31 0.10
r2l
u2r
```{r}
# Decision Tree using 5 attrbutes and 100000 training tuples and 25974 testing
decision_tree_model <- rpart(result ~ protocol_type+srv_rerror_rate+flag+rerror_rate+logged_in, data = training2[1:100000,], method = "class")
# Predicting:
decision_tree_pred <- predict(decision_tree_model, testing_data[1:25974,], type = "class")
decision_tree_pred
right <- decision_tree_pred==testing_data[1:25974,"result"]
answer<-table(decision_tree_pred,testing_data[1:25974,"result"])
answer
round(prop.table(answer,1)*100,2)
# Plot of the Decision Tree
rpart.plot(decision_tree_model, main = "Classification Tree",
extra = 102, under = TRUE, faclen = 0)
# Test results on our subTesting data set:
confusionMatrix(prediction1, subTesting$classe)
```
> answer
decision_tree_pred dos normal probe r2l u2r
dos 8484 636 200 14 0
normal 364 12925 357 190 9
probe 619 337 1836 3 0
r2l 0 0 0 0 0
u2r 0 0 0 0 0
> round(prop.table(answer,1)*100,2)
decision_tree_pred dos normal probe r2l u2r
dos 90.89 6.81 2.14 0.15 0.00
normal 2.63 93.36 2.58 1.37 0.07
probe 22.15 12.06 65.69 0.11 0.00
r2l
u2r