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.

References:

[1] Nadia Chaabouni et al., “Network Intrusion Detection for IoT Security Based on Learning Techniques,” IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2671-2701, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ruijie Zhao et al., “A Novel Intrusion Detection Method Based on Lightweight Neural Network for Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9960-9972, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Geethapriya Thamilarasu, Adedayo Odesile, and Andrew Hoang “An Intrusion Detection System for Internet of Medical Things,” IEEE Access, vol. 8, pp. 181560-181576, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Sana Ullah Jan et al., “Toward a Lightweight Intrusion Detection System for the Internet of Things,” IEEE Access, vol. 7, pp. 4245042471, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Abdullah Alsaedi et al., “TON_IoT Telemetry Dataset: A New Generation Dataset of IoT and IIot for Data-Driven Intrusion Detection Systems,” IEEE Access, vol. 8, pp. 165130-165150, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Seyyed Meysam Tabatabaie Nezhad, Mahboubeh Nazari, and Ebrahim A. Gharavol, “A Novel DoS and DDoS Attacks Detection Algorithm Using ARIMA Time Series Model and Chaotic System in Computer Networks,” IEEE Communications Letters, vol. 20, no. 4, pp. 700-703, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Kai Yang et al., “Active Learning for Wireless IoT Intrusion Detection,” IEEE Wireless Communications, vol. 25, no. 6, pp. 19-25, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Abdulaziz Fatani et al., “IoT Intrusion Detection System Using Deep Learning and Enhanced Transient Search Optimization,” IEEE Access, vol. 9, pp. 123448-123464, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Mohamed Abdel-Basset et al., “Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment,” IEEE Transactions on Industrial Informatics, vol. 17, no. 11, pp. 7704-7715, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Nour Moustafa, Benjamin Turnbull, and Kim-Kwang Raymond Choo, “An Ensemble Intrusion Detection Technique Based on Proposed Statistical Flow Features for Protecting Network Traffic of Internet of Things,” IEEE Internet of Things Journal, vol. 6, no. 3, pp. 4815-4830, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Ghada Abdelmoumin, Danda B. Rawat, and Abdul Rahman, “On the Performance of Machine Learning Models for Anomaly-Based Intelligent Intrusion Detection Systems for the Internet of Things,” IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4280-4290, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Jie Gu, and Shan Lu, “An Effective Intrusion Detection Approach Using SVM with Naïve Bayes Feature Embedding,” Computers & Security, vol. 103, 2021. [CrossRef] [Google Scholar] [Publisher Link]
[13] K.V.V.N.L. Sai Kiran et al., “Building an Intrusion Detection System for IoT Environment using Machine Learning Techniques,” Procedia Computer Science, vol. 171, pp. 2372-2379, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Mojtaba Eskandari et al., “Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices,” IEEE Internet of Things Journal, vol. 7, no. 8, pp. 6882-6897, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Jiyeon Kim et al., “CNN-Based Network Intrusion Detection Against Denial-of-Service Attacks,” Electronics, vol. 9, no. 6, pp. 1-21, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ming Zhong, Yajin Zhou, and Gang Chen, “Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods,” Sensors, vol. 21, no. 4, pp. 1-21, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Jesus Pacheco et al., “Artificial Neural Networks-Based Intrusion Detection System for Internet of Things Fog Nodes,” IEEE Access, vol. 8, pp. 73907-73918, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Ying Zhang, Peisong Li, and Xinheng Wang, “Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network,” IEEE Access, vol. 7, pp. 31711-31722, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Olakunle Ibitoye, Omair Shafiq, and Ashraf Matrawy, “Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection In IoT Networks,” IEEE Global Communications (GLOBECOM), Waikoloa, HI, USA, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Ayesha S. Dina, and D. Manivannan, “Intrusion Detection Based on Machine Learning Techniques in Computer Networks,” Internet of Things, vol. 16, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Wajdi Alhakami et al., “Network Anomaly Intrusion Detection Using a Nonparametric Bayesian Approach and Feature Selection,” IEEE Access, vol. 7, pp. 52181-52190, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Tarek Gaber et al., “Industrial Internet of Things Intrusion Detection Method Using Machine Learning and Optimization Techniques,” Wireless Communications and Mobile Computing, vol. 2023, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Sydney Mambwe Kasongo, “An Advanced Intrusion Detection System for IIot Based on GA and Tree Based Algorithms,” IEEE Access, vol. 9, pp. 113199-113212, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Bayu Adhi Tama, Marco Comuzzi, and Kyung-Hyune Rhee, “TSE-IDS: A Two-Stage Classifier Ensemble for Intelligent AnomalyBased Intrusion Detection System,” IEEE Access, vol. 7, pp. 94497-94507, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Xianwei Gao et al., “An Adaptive Ensemble Machine Learning Model for Intrusion Detection,” IEEE Access, vol. 7, pp. 82512-82521, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Treepop Wisanwanichthan, and Mason Thammawichai, “A Double-Layered Hybrid Approach for Network Intrusion Detection System Using Combined Naive Bayes and SVM,” IEEE Access, vol. 9, pp. 138432-138450, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Kaiyuan Jiang et al., “Network Intrusion Detection Combined Hybrid Sampling with Deep Hierarchical Network,” IEEE Access, vol. 8, pp. 32464-32476, 2020.
[CrossRef] [Google Scholar] [Publisher Link]