Classification of Botnet attacks on IoT devices using stacked ensembles in the H2O framework. Dataset used is the N-BAIoT dataset

The Scripts in this repository make stacked ensemble using a metalearning for over 21 different model configurations to improve the prediction of botnet attacks in IoT Devices.

Navigate to your working directory.

`git clone https://github.com/sagarhowal/botnet_attack_detection_using_h2o.git .'

Run Scripts:

DataPreparation.R StackedEnsemble.R

Models used are similar to the ones on the h2o repository example (with some changes):

#generating new custom learner wrappers: h2o.glm.1 <- function(..., alpha = 0.0) h2o.glm.wrapper(..., alpha = alpha) h2o.glm.2 <- function(..., alpha = 0.5) h2o.glm.wrapper(..., alpha = alpha) h2o.glm.3 <- function(..., alpha = 1.0) h2o.glm.wrapper(..., alpha = alpha) h2o.randomForest.1 <- function(..., ntrees = 200, nbins = 50, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, nbins = nbins, seed = seed) h2o.randomForest.2 <- function(..., ntrees = 200, sample_rate = 0.75, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, sample_rate = sample_rate, seed = seed) h2o.randomForest.3 <- function(..., ntrees = 200, sample_rate = 0.85, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, sample_rate = sample_rate, seed = seed) h2o.randomForest.4 <- function(..., ntrees = 200, nbins = 50, balance_classes = TRUE, seed = 1) h2o.randomForest.wrapper(..., ntrees = ntrees, nbins = nbins, balance_classes = balance_classes, seed = seed) h2o.gbm.1 <- function(..., ntrees = 100, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, seed = seed) h2o.gbm.2 <- function(..., ntrees = 100, nbins = 50, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, nbins = nbins, seed = seed) h2o.gbm.3 <- function(..., ntrees = 100, max_depth = 10, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, max_depth = max_depth, seed = seed) h2o.gbm.4 <- function(..., ntrees = 100, col_sample_rate = 0.8, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, col_sample_rate = col_sample_rate, seed = seed) h2o.gbm.5 <- function(..., ntrees = 100, col_sample_rate = 0.7, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, col_sample_rate = col_sample_rate, seed = seed) h2o.gbm.6 <- function(..., ntrees = 100, col_sample_rate = 0.6, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, col_sample_rate = col_sample_rate, seed = seed) h2o.gbm.7 <- function(..., ntrees = 100, balance_classes = TRUE, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, balance_classes = balance_classes, seed = seed) h2o.gbm.8 <- function(..., ntrees = 100, max_depth = 3, seed = 1) h2o.gbm.wrapper(..., ntrees = ntrees, max_depth = max_depth, seed = seed)

#Optional Deep Learning Models h2o.deeplearning.1 <- function(..., hidden = c(500,500), activation = "Rectifier", epochs = 50, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed) h2o.deeplearning.2 <- function(..., hidden = c(200,200,200), activation = "Tanh", epochs = 50, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed) h2o.deeplearning.3 <- function(..., hidden = c(500,500), activation = "RectifierWithDropout", epochs = 50, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed) h2o.deeplearning.4 <- function(..., hidden = c(500,500), activation = "Rectifier", epochs = 50, balance_classes = TRUE, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, balance_classes = balance_classes, seed = seed) h2o.deeplearning.5 <- function(..., hidden = c(100,100,100), activation = "Rectifier", epochs = 50, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed) h2o.deeplearning.6 <- function(..., hidden = c(50,50), activation = "Rectifier", epochs = 50, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed) h2o.deeplearning.7 <- function(..., hidden = c(100,100), activation = "Rectifier", epochs = 50, seed = 1) h2o.deeplearning.wrapper(..., hidden = hidden, activation = activation, seed = seed)

Change the models configurations while training.