Performance Analysis of Tree-Based and Deep Learning Algorithms for Developing Distributed Secure Systems in IoT: A Comparative Study

Abstract

Notably, IoT device utilization has experienced a substantial wave recently, and ensuring these devices' privacy and security has become a critical concern. ML-based security approaches are promising for IoT network protection against security concerns. This study provides a proximate analysis of tree-based and deep-learning algorithms for securing IoT domains. Specifically, we evaluate Decision Tree, RandomForest, XGBoost, Catboost, Extreme Tree, Light GMB, Adaptive Boosting, CNN, LSTM, MLP, GRU, and Autoencoder on four publicly available datasets - IoT23, CICID2017, EdgeIIoT, BotnetIoT and Contiki OS and Cooja simulation were used to generate a dataset featuring various RPL attacks. To assess the performance of a model, we measure its accuracy, precision, recall, and F1-score metrics. Our discoveries indicate that tree-based algorithms outperform deep learning algorithms regarding training time, memory usage, and interpretability while gaining comparable or even better detection accurateness. Conversely, deep-learning algorithms exhibit higher detection rates for rare or previously unseen attacks; their proficiency in detecting complex patterns and relationships within a given dataset has demonstrated remarkable efficacy in data analysis and classification tasks. We conclude that both tree-based and deep learning algorithms have their strengths and weaknesses, and in the IoT environment, one should base the choice of the algorithm on requirements and constraints. Our research shows hybrid approaches combining algorithm strengths can establish secure, distributed IoT systems