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131 | # Import necessary libraries
library(data.table)
library(caret)
library(h2o)
localH2O = h2o.init()
# Importing the Network Intrusion Data set
dataset <- fread("2020.10.01.csv")
dataset <- na.omit(dataset)
dataset <- dataset[, -c(12, 13)]
correlationSet <- dataset
# Encoding 'label' as Catagorical Variable
dataset$label <- factor(dataset$label,
levels = c("benign", "malicious", "outlier"),
labels = c(1, 2, 3))
correlationSet$label <- factor(correlationSet$label,
levels = c("benign", "malicious", "outlier"),
labels = c(1, 2, 3))
correlationSet$label <- as.numeric(correlationSet$label)
# Remove Redundant Features - First Find Correlated Features
correlationMatrix <- cor(correlationSet)
highlyCorrelated <- findCorrelation(correlationMatrix, cutoff=0.5)
print(highlyCorrelated)
df <- dataset[, c(8,2,7,3,5,12,13)]
df <- as.h2o(dataset)
head(dataset[, c(8,2,7,3,5,12,13)])
# set the predictor and response columns
predictors <- c("num_pkts_in", "bytes_in", "num_pkts_out", "bytes_out",
"dest_port", "total_entropy")
response <- "label"
# split the dataset into train and test sets
df_splits <- h2o.splitFrame(data = df, ratios = 0.8)
train <- df_splits[[1]]
test <- df_splits[[2]]
# Build and train Deep learning model:
dl <- h2o.deeplearning(x = 1:6,
y = "label",
distribution = "multinomial",
hidden = c(1),
epochs = 100,
train_samples_per_iteration = -1,
reproducible = TRUE,
activation = "Tanh",
single_node_mode = FALSE,
balance_classes = FALSE,
force_load_balance = FALSE,
seed = 23123,
score_training_samples = 0,
score_validation_samples = 0,
training_frame = df,
stopping_rounds = 0)
# Eval performance of deep learning model:
perf <- h2o.performance(dl)
perf
# Generate predictions on a test set (if necessary):
pred <- h2o.predict(dl, newdata = df)
summary(dl)
# Save the model
dl_model <- h2o.saveModel(object = dl,
path = "/Users/lucifer/Documents/projects/NetworkIntrusionDetection/models",
force = TRUE)
print(dl_model)
# Build and train distributed random forest model:
drf <- h2o.randomForest(x = predictors,
y = response,
ntrees = 10,
max_depth = 5,
min_rows = 10,
calibration_frame = test,
binomial_double_trees = TRUE,
training_frame = train,
validation_frame = test)
# Eval Performance of distributed random forest model:
h2o.performance(drf)
summary(dl)
# Save the model
drf_model <- h2o.saveModel(object = drf,
path = "/Users/lucifer/Documents/projects/NetworkIntrusionDetection/models",
force = TRUE)
# Build and train the Gradient Boosting machine model:
gbm <- h2o.gbm(x = predictors,
y = response,
nfolds = 5,
seed = 1111,
keep_cross_validation_predictions = TRUE,
training_frame = df)
# Eval Performance of GBM model:
h2o.performance(gbm)
summary(dl)
# Save the model
gbm_model <- h2o.saveModel(object = gbm,
path = "/Users/lucifer/Documents/projects/NetworkIntrusionDetection/models",
force = TRUE)
# Build and train the Naive Bayes model:
nb <- h2o.naiveBayes(x = predictors,
y = response,
training_frame = df,
laplace = 0,
nfolds = 5,
seed = 1234)
# Eval performance of the Naive Bayes:
h2o.performance(nb)
summary(nb)
nb_model <- h2o.saveModel(object = nb,
path = "/Users/lucifer/Documents/projects/NetworkIntrusionDetection/models",
force = TRUE)
|
