Enhancing iot network security through advanced data preprocessing and hybrid firefly-salp swarm optimized deep CNN-based intrusion detection
Abstract
This concept addresses the imperative need for robust Intrusion Detection system (IDs) in Internet of Things (IoT) networks by presenting a comprehensive approach that integrates advanced data preprocessing techniques and Deep Convolutional Neural Network (DCNN) based IDS. The process commences with raw and inherently noisy data generated by IoT sensors. To fortify the detection capabilities, a sequence of preprocessing steps is applied, including data cleaning, one-hot encoding and normalization, ensuring the prepared data is resilient to outliers and irrelevant information while being conducive to Deep Learning (DL) models. The core of the proposed system is a DCNN, adept at capturing sequential patterns within diverse and dynamic IoT data. To further optimize the performance of the DCNN, a hybrid firefly-salp swarm optimization algorithm is employed. This hybrid approach leverages the strengths of both Firefly and salp swarm optimization techniques (FFA-SSA), enhancing the model's ability to identify potential security threats effectively. The synergy of advanced data preprocessing and nature-inspired optimization methods not only strengthens the security posture of IoT networks but also contributes to the resilience and adaptability of intrusion detection systems. The presented concept signifies a crucial step towards ensuring more secure and resilient IoT deployments, acknowledging the pivotal role played by innovative techniques in preparing data and optimizing deep learning models for enhanced cybersecurity.
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References
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