Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

TL;DR

This paper introduces a deep transfer learning-based intrusion detection system for IoT that improves accuracy, efficiency, and dependability by leveraging effective attribute selection and a ResNet model evaluated on real-world data.

Contribution

The paper presents a novel deep transfer learning-based IDS with effective attribute selection and a ResNet architecture tailored for heterogeneous IoT environments.

Findings

Outperforms existing IDS approaches in accuracy and efficiency

Demonstrates robustness and dependability on real-world IoT data

Shows improved scalability in diverse IoT scenarios

Abstract

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.