Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method

TL;DR

This paper presents a comprehensive anomaly detection framework for IoT security that combines data preprocessing, feature selection via a genetic algorithm, and an ensemble classifier, achieving high accuracy and robustness against cyber threats.

Contribution

It introduces a novel combination of genetic algorithm-based feature selection and ensemble classification for IoT anomaly detection, improving accuracy and efficiency.

Findings

Achieved 98% detection accuracy

Reduced false positive rate to 10%

Enhanced robustness against cyber threats

Abstract

The rapid growth of the Internet of Things (IoT) has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service (DoS) attacks, anomalous traffic, and data manipulation due to decentralized architectures and limited resources. To address these issues, this paper proposes an advanced anomaly detection framework with three main phases. First, data preprocessing is performed using the Median KS Test to remove noise, handle missing values, and balance datasets for cleaner input. Second, a feature selection phase employs a Genetic Algorithm combined with eagle inspired search strategies to identify the most relevant features, reduce dimensionality, and improve efficiency without sacrificing accuracy. Finally, an ensemble classifier integrates Decision Tree, Random Forest, and XGBoost algorithms to achieve accurate and reliable anomaly detection. The proposed model demonstrates high adaptability and scalability across diverse IoT environments. Experimental results show that it outperforms existing methods by achieving 98 percent accuracy, 95 percent detection rate, and reductions in false positive (10 percent) and false negative (5 percent) rates. These results confirm the framework effectiveness and robustness in improving IoT network security against evolving cyber threats.