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A Deep Intelligent Attack Detection Framework for Fog-Based IoT Systems.

Surya Pavan Kumar Gudla1, Sourav Kumar Bhoi2, Soumya Ranjan Nayak3

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This study introduces a Long Short-Term Memory Deep Learning (LSTM-DL) model for fog-based Internet of Things (IoT) attack detection. The LSTM-DL model effectively predicts various network threats with high accuracy, outperforming other deep learning models.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Fog computing enhances IoT services but introduces security vulnerabilities during data transmission between fog nodes and the cloud.
  • Internet of Things (IoT) devices have limited computational resources, making traditional Deep Learning (DL) models difficult to deploy directly.
  • Early detection of network threats like Distributed Denial of Service (DDoS) is crucial for IoT system integrity.

Purpose of the Study:

  • To propose a novel fog-based framework for detecting and predicting security attacks in IoT environments.
  • To evaluate the performance of a Long Short-Term Memory Deep Learning (LSTM-DL) model for attack prediction on IoT end devices.
  • To compare the proposed LSTM-DL model against other advanced DL models for network threat detection.

Main Methods:

  • Developed and deployed a trained LSTM-DL model on fog nodes to predict end-user behavior and identify network attacks.
  • Conducted simulations using Python, comparing the LSTM-DL model with Deep Neural Multilayer Perceptron (DNMLP), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Units (GRU), Hybrid Ensemble Model (HEM), and Convolutional Neural Network (CNN)+LSTM.
  • Utilized benchmark datasets including DDoS-SDN, NSLKDD, UNSW-NB15, and IoTID20 for comprehensive performance evaluation.

Main Results:

  • The LSTM-DL model achieved superior binary classification performance, with accuracies of 99.70%, 99.12%, 94.11%, and 99.88% on the tested datasets.
  • While DNMLP demonstrated faster communication behavior detection time (CBDT), the LSTM-DL model exhibited superior assault prediction capabilities.
  • Performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC curves were used to validate the model's effectiveness.

Conclusions:

  • The proposed LSTM-DL model offers a highly accurate and effective solution for fog-based IoT attack detection, addressing the limitations of deploying DL on resource-constrained devices.
  • The LSTM-DL model significantly outperforms other compared DL models in predicting various network assaults within the fog computing architecture.
  • This research highlights the potential of LSTM-DL in enhancing the security and reliability of IoT systems through advanced threat prediction.