Brute Force Attacks Detection on IoT Networks using Deep Learning Techniques
Shubham A. Shirodkar · International Journal of Advanced Research in Science Communication and Technology · 2023
The Internet of Things (IoT) sector is expanding quickly, and its applications are becoming more prevalent in our day-to-day lives. Various protocols are used to control communication between IoT devices. The Message Queue Telemetry Protocol (MQTT), a simple and trustworthy communication protocol, is a well-known illustration of these protocols. However, MQTT-IoT networks have been the target of cyberattacks, which highlights the need for an effective intrusion detection system for spotting such attempts. The brute force attack is a common sort of such attacks. We suggest deep learning in this study as a means of automatically identifying brute force assaults on MQTT-IoT networks. We train the deep learning model with a large number of instances and a flow-based feature using the MQTT-IoT-IDS2020 dataset. With more than 99% accuracy in differentiating between regular and brute force attacks, the classification model is quite accurate in detecting such attempts