Lightweight Hybrid Framework for IoT Security Using Optimized Random Forest and Adaptive Feature Selection in Edge-Cloud Architecture
Subject Areas : مهندسی برق و کامپیوتر
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Keywords: Internet of Things, machine learning, anomaly detection, security, optimized random forest (ORF), edge-cloud architecture.,
Abstract :
Security in Internet of Things (IoT) infrastructures has become a critical challenge due to the rising cyber threats, such as Denial-of-Service (DoS) attacks, data breaches, and device manipulation. IoT devices, as core components of these systems, are highly vulnerable to various attacks and anomalies due to their continuous network connectivity and handling of sensitive data, potentially leading to system disruptions, confidential data leaks, and financial or human losses. Despite significant advancements in IoT security, challenges such as the diversity and complexity of attacks, heterogeneous and voluminous sensor data, variations in normal system behavior, scarcity of high-quality training data, and the need for scalable methods continue to hinder accurate and timely attack detection. This study proposes a lightweight and intelligent hybrid framework based on an Optimized Random Forest (ORF) and adaptive feature selection to enhance IoT system security. The performance of various machine learning algorithms, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and ORF, was evaluated using the DS2OS dataset. Results demonstrated that all models achieved accuracies ranging from 0.9844 to 0.9948, with RF and ORF exhibiting superior performance at an accuracy of 0.9943, precision of 0.9943, recall of 0.9943, and an F1-score of 0.9937. Furthermore, integrating the Particle Swarm Optimization (PSO) algorithm reduced the false positive rate to 0.57%, while the Edge-Cloud architecture improved processing time by 40%. Compared to existing approaches, the proposed method reduced memory consumption by 29%, offering a scalable solution for IoT security.
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