Abstract:
The proliferation of IoT devices has heightened
their susceptibility to cyberattacks, particularly botnets.
Conventional security methods frequently prove inadequate
because of the restricted processing capabilities of IoT devices.
This paper suggests utilizing machine learning methods to
enhance the detection of attacks in Internet of Things (IoT)
environments. The paper presents a novel approach to detect
different botnet assaults on IoT devices by utilizing ML methods
such as XGBoost, Random Forest, LightGBM, and Decision
Tree. These algorithms were examined using the N-BaIoT
dataset to classify multi-class botnet attacks and were
specifically designed to accommodate the limitations of IoT
devices. The technique comprises the steps of data preparation,
preprocessing, classifier training, and decision-making. The
algorithms achieved high detection accuracy rates: XGBoost
(99.18%), Random Forest (99.20%), LGBM (99.85%), and
Decision Tree (99.17%). The LGBM model demonstrated
exceptional performance. The incorporation of the attack
evaluation model greatly enhanced the identification of botnets
in IoT networks. The paper displays the efficacy of machine
learning techniques in identifying botnet assaults in IoT
networks. The models generated exhibit exceptional accuracy
and can be seamlessly integrated into existing cybersecurity
systems.
