Enhancing intrusion detection in IoT networks using machine learning-based feature selection and ensemble models

Internet of Things (IoT) technology has evolved significantly, transitioning from personal devices to powering smart cities and global deployments across diverse industries. However, security challenges arise due to diverse devices using various protocols and having limited computational capabilities, leading to vulnerabilities and potential intrusions in IoT networks. This paper addresses the challenge of intrusion detection in IoT by introducing a heterogeneous machine learning-based stack classifier model for IoT data. The model employs feature selection and ensemble modelling to investigate and enhance key classification metrics for intrusion detection of IoT data. This approach comprises two core components: the utilization of the K-Best algorithm for feature selection, extracting the top 15 critical features and the construction of an ensemble model incorporating various traditional machine learning models. The integration of these components harnesses information from selected features and leverages the collective strength of individual models to enhance classification performance. Using the ‘Ton IoT dataset,’ our experiments compare the ensemble model with individual ones. This research aims to improve key classification metrics for IoT intrusion detection, focusing on accuracy, precision, recall and F1 score. Through rigorous experimentation and comparisons, the proposed ensemble approach showcases exceptional performance, providing a robust solution to fortify IoT network security

Innovative Energy-Efficient Proxy Re-Encryption for Secure Data Exchange in Wireless Sensor Networks

In the realm of wireless sensor networks (WSNs), preserving data integrity, privacy, and security against cyberthreats is paramount. Proxy re-encryption (PRE) plays a pivotal role in ensuring secure intra-network communication. However, existing PRE solutions encounter persistent challenges, including processing delays due to the transfer of substantial data to the proxy for re-encryption and the computational intensity of asymmetric cryptography. This study introduces an innovative PRE scheme that is meticulously customized for WSNs to enhance the secure communication between nodes within the network and external data server. The proposed PRE scheme optimizes efficiency by integrating lightweight symmetric and asymmetric cryptographic techniques, thereby minimizing computational costs during PRE operations and conserving energy for resource-constrained nodes. In addition, the scheme incorporates sophisticated key management and digital certificates to ensure secure key generation and distribution, which in turn, facilitates seamless authentication and scalable data sharing among the entities in WSN. This scheme maintains sensor-node data encryption and delegates secure re-encryption tasks exclusively to cluster heads, thereby reinforcing data privacy and integrity. Comprehensive evaluations of security, performance, and energy consumption validated the robustness of the scheme. The results confirm that the proposed PRE scheme significantly enhances the security, efficiency, and overall network lifetime of WSNs