IoT Intrusion Detection Enhancement: Data Preprocessing and Dolphin POD-Optimized Deep RNN

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 6

Year of Publication : 2024

Authors : Mohsin Ali, Jitendra Choudhary, D. Srinivasa Rao, Ritesh Jain, Govinda Patil

10.14445/23488549/IJECE-V11I6P112

How to Cite?

Mohsin Ali, Jitendra Choudhary, D. Srinivasa Rao, Ritesh Jain, Govinda Patil, "IoT Intrusion Detection Enhancement: Data Preprocessing and Dolphin POD-Optimized Deep RNN," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 6, pp. 137-147, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I6P112

Abstract:

In the evolving landscape of IoT (Internet of Things) networks, the security of collected data is of paramount importance. This abstract introduces a novel concept that combines data preprocessing techniques with the optimization prowess of Dolphin Pod Optimization to enhance the effectiveness of Intrusion Detection Systems (IDS) based on Deep Recurrent Neural Networks (RNN). The process begins with raw data generated by IoT sensors, which are inherently noisy and diverse. To prepare this data for robust intrusion detection, a series of preprocessing steps are applied. This includes data cleaning to remove outliers and irrelevant information, one-hot encoding to transform categorical variables into numerical representations, and data normalization to scale data into a consistent range. The preprocessed data is then fed into a Deep RNN-based Intrusion Detection System (IDS), which leverages the temporal dependencies within the data to identify potential security threats. The Deep RNN excels at capturing sequential patterns in IoT data, making it well-suited for intrusion detection tasks. To optimize the performance of the Deep RNN, Dolphin Pod Optimization (DPO) is employed. DPO is a nature-inspired optimization algorithm inspired by the coordinated hunting behavior of dolphins. It adapts and fine-tunes the parameters of the RNN, optimizing its architecture and hyperparameters for superior intrusion detection accuracy. This optimization process is guided by the collective intelligence of the DPO algorithm, allowing it to navigate the complex parameter space effectively. The combination of data preprocessing and Dolphin Pod Optimization results in an IDS that exhibits enhanced accuracy and efficiency in detecting intrusions within IoT networks. By effectively cleaning and normalizing data and fine-tuning the RNN parameters through DPO, the system is capable of providing real-time security monitoring and threat detection, thus contributing to the overall robustness and reliability of IoT environments. This concept underscores the significance of advanced data preprocessing techniques and nature-inspired optimization methods in strengthening the security of IoT networks, paving the way for more secure and resilient IoT deployments.

Keywords:

IoT, Intrusion detection systems, Deep RNN, Dolphin pod optimization, Security threats.

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