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Transfer-Learning-Based Intrusion Detection Framework in IoT Networks.

Eva Rodríguez¹, Pol Valls¹, Beatriz Otero¹

¹Department of Computer Architecture, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain.

Sensors (Basel, Switzerland)

|August 12, 2022

Summary

This summary is machine-generated.

Transfer learning (TL) effectively detects zero-day cyberattacks in Internet of Things (IoT) networks, even with limited data. This approach enhances intrusion detection systems (IDSs) for 5G environments, outperforming traditional deep learning methods.

Keywords:

IoT networks convolutional neural network cybersecurity intrusion detection systems transfer learning

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Area of Science:

Cybersecurity
Network Security
Artificial Intelligence

Background:

Internet of Things (IoT) cyberattacks, particularly zero-day exploits, are rapidly increasing due to inherent network vulnerabilities.
Traditional Intrusion Detection Systems (IDS) using machine learning (ML) and deep learning (DL) struggle with the scarcity of labeled data in IoT environments.
Existing DL-based IDSs require large, balanced datasets, which are often unavailable for IoT networks, hindering effective zero-day attack detection.

Purpose of the Study:

To propose an efficient intrusion detection framework leveraging transfer learning (TL) for detecting zero-day cyberattacks in 5G IoT scenarios.
To address the challenges of unbalanced and scarce labeled datasets in IoT network security.
To enhance the detection capabilities of IDSs against novel and evolving cyber threats.

Main Methods:

Development of an intrusion detection framework utilizing transfer learning (TL), knowledge transfer, and model refinement.
Implementation of a TL model based on Convolutional Neural Networks (CNNs).
Creation of three specialized datasets for evaluating the framework's performance in detecting diverse zero-day attacks.

Main Results:

The proposed TL-based framework demonstrated high accuracy and a low false prediction rate (FPR) in detecting zero-day attacks.
The framework achieved superior detection rates for various known and zero-day attack families compared to previous DL-based IDSs.
Experimental validation confirmed the framework's effectiveness in 5G IoT environments with limited labeled data.

Conclusions:

Transfer learning (TL) is a highly effective technique for improving the detection of cyberattacks, especially zero-day threats, in resource-constrained IoT environments.
The proposed framework offers a viable solution for enhancing the security of 5G IoT networks facing data scarcity challenges.
TL-based IDSs present a promising advancement over traditional DL approaches for robust IoT cybersecurity.