IoT Security: Botnet detection in IoT using Machine learning

TL;DR

This paper proposes a machine learning-based model to detect and mitigate botnet-driven DDoS attacks in IoT networks, utilizing feature engineering, SMOTE, and multiple algorithms to improve security.

Contribution

It introduces an innovative ML model for IoT botnet detection, comparing algorithms and optimizing performance with feature engineering and data balancing techniques.

Findings

KNN achieved highest accuracy among tested algorithms.

Synthetic minority oversampling improved detection on imbalanced data.

Model effectively identifies botnet-based DDoS attacks in IoT environments.

Abstract

The acceptance of Internet of Things (IoT) applications and services has seen an enormous rise of interest in IoT. Organizations have begun to create various IoT based gadgets ranging from small personal devices such as a smart watch to a whole network of smart grid, smart mining, smart manufacturing, and autonomous driver-less vehicles. The overwhelming amount and ubiquitous presence have attracted potential hackers for cyber-attacks and data theft. Security is considered as one of the prominent challenges in IoT. The key scope of this research work is to propose an innovative model using machine learning algorithm to detect and mitigate botnet-based distributed denial of service (DDoS) attack in IoT network. Our proposed model tackles the security issue concerning the threats from bots. Different machine learning algorithms such as K- Nearest Neighbour (KNN), Naive Bayes model and Multi-layer Perception Artificial Neural Network (MLP ANN) were used to develop a model where data are trained by BoT-IoT dataset. The best algorithm was selected by a reference point based on accuracy percentage and area under the receiver operating characteristics curve (ROC AUC) score. Feature engineering and Synthetic minority oversampling technique (SMOTE) were combined with machine learning algorithms (MLAs). Performance comparison of three algorithms used was done in class imbalance dataset and on the class balanced dataset.