Distributed Denial of Services (DDoS) & IoT Botnet Malware Identification Using Machine Learning & Deep Learning Models

In this work, distributed denial of services (DDoS) and IoT botnet attacks detection has been performed using machine learning (ML) and deep learning (DL) models. For the implementation of the proposed work DDoS attacks and IoT botnet datasets are used. These instances are collected by the implementation of Mirai and BASHLITE. The used dataset comprises of 7999 instances and each instance has 29 attributes. The collected instances are pre-processed and eliminate the redundant attributes. Therefore, finally a set of 10 attributes are selected for the experiments. After this dataset is divided into training and testing set. By using training set, machine leaning models (KNN classifier, logistic regression, SVM model, random forest model) and deep learning models (CNN and LSTM) are trained and validated using testing set. After the experiments it has been found that the deep learning-based LSTM model obtainedoutstanding performance in terms of accuracy. The obtained testing accuracy for LSTM model is 99.80 % and 99.82 % for training accuracy.