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FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning.

Rabia Khan1, Noshina Tariq1, Muhammad Ashraf2

  • 1Department of Avionics Engineering, Air University, Islamabad 44000, Pakistan.

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|September 14, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces FL-DSFA, a federated learning technique to detect selective forwarding attacks in the Internet of Things (IoT). It enhances IoT security by accurately identifying routing attacks with high precision and efficiency.

Keywords:
Decreased Rank (DR)Hello Food (HF)Internet of Things (IoT)IoT Routing Attack Dataset (IRAD)Linear Discriminant Analysis (LDA)deep learningfederated learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Machine Learning

Background:

  • The Internet of Things (IoT) enables seamless device integration but faces significant security challenges.
  • Routing attacks, particularly selective forwarding attacks on the Routing Protocol for Low-Power and Lossy Networks (RPL), pose a severe threat to IoT systems.
  • These attacks target critical components like control messages and routing topologies, leading to potential data loss and system damage.

Purpose of the Study:

  • To develop and evaluate a novel federated learning-based detection technique (FL-DSFA) for identifying selective forwarding attacks in IoT networks.
  • To enhance the security and reliability of IoT routing protocols against sophisticated attacks.
  • To improve the efficiency and privacy of attack detection mechanisms in resource-constrained IoT environments.

Main Methods:

  • Implementation of a lightweight federated learning model (FL-DSFA) utilizing the IoT Routing Attack Dataset (IRAD).
  • Inclusion of attack types such as Hello Flood (HF), Decreased Rank (DR), and Version Number (VN) for comprehensive detection.
  • Assessment of binary classification algorithms including Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB) for training efficiency.

Main Results:

  • The FL-DSFA technique, particularly with SVM and KNN classifiers, demonstrated high accuracy and efficient runtime performance during training.
  • The proposed system achieved exceptional performance metrics: 97.50% prediction precision, 95% accuracy, 98.33% recall rate, and 97.01% F1 score.
  • Comparative analysis confirmed the superiority of the proposed method over existing research in terms of classification accuracy, scalability, and privacy.

Conclusions:

  • Federated learning offers a robust solution for detecting selective forwarding attacks in IoT networks.
  • The FL-DSFA technique provides a scalable, efficient, and privacy-preserving approach to bolster IoT security.
  • The study highlights the potential of machine learning in securing critical IoT infrastructure against evolving cyber threats.