A State-of-the-Art Review on IoT botnet Attack Detection

TL;DR

This paper reviews current IoT botnet attack detection methods, emphasizing machine learning techniques and analyzing their effectiveness using the Bot-IoT dataset, highlighting recent advancements and challenges in securing IoT devices.

Contribution

It provides a comprehensive overview of IoT botnet attack detection frameworks and compares various machine learning-based techniques using a realistic dataset.

Findings

Machine learning techniques show promise in detecting IoT botnets.

The Bot-IoT dataset enables realistic evaluation of detection methods.

Challenges remain in achieving high accuracy and low false positives.

Abstract

The Internet as we know it Today, comprises several fundamental interrelated networks, among which is the Internet of Things (IoT). Despite their versatility, several IoT devices are vulnerable from a security perspective, which renders them as a favorable target for multiple security breaches, especially botnet attacks. In this study, the conceptual frameworks of IoT botnet attacks will be explored, alongside several machinelearning based botnet detection techniques. This study also analyzes and contrasts several botnet Detection techniques based on the Bot-IoT Dataset; a recent realistic IoT dataset that comprises state-of-the-art IoT botnet attack scenarios.