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

arXiv: https://arxiv.org/abs/2204.04837 · HTML: https://mlsanity.com/paper/2204.04837/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 39 references that are in the map.
- First posted on arXiv 11 Apr 2022.

## Builds on (most cited first, 5 of 5)

- [Omni SCADA Intrusion Detection Using Deep Learning Algorithms](https://mlsanity.com/paper/1908.01974/) (2019)
- [Cognitive Internet of Vehicles: Motivation, Layered Architecture and Security Issues](https://mlsanity.com/paper/1912.03356/) (2019)
- [Dependable Structural Helath Monitoring Using Wireless Sensor Networks](https://mlsanity.com/paper/1509.06065/) (2015)
- [Security Considerations for Internet of Things: A Survey](https://mlsanity.com/paper/2006.10591/) (2020)
- [Con-Pi: A Distributed Container-based Edge and Fog Computing Framework](https://mlsanity.com/paper/2101.03533/) (2021)

## Built on by (most cited first, 4 of 4)

- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [Handling Out-of-Distribution Data: A Survey](https://mlsanity.com/paper/2507.21160/) (2025)
- [Sampling Preferences Yields Simple Trustworthiness Scores](https://mlsanity.com/paper/2506.03399/) (2025)
- [eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem](https://mlsanity.com/paper/2604.26219/) (2026)

## Abstract (arXiv)

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.