# Class-Incremental Learning for Wireless Device Identification in IoT

arXiv: https://arxiv.org/abs/2105.06381 · HTML: https://mlsanity.com/paper/2105.06381/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 8 May 2021.

## Builds on (most cited first, 14 of 14)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Continual Learning with Deep Generative Replay](https://mlsanity.com/paper/1705.08690/) (2017)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Mnemonics Training: Multi-Class Incremental Learning without Forgetting](https://mlsanity.com/paper/2002.10211/) (2020)
- [Generative Feature Replay For Class-Incremental Learning](https://mlsanity.com/paper/2004.09199/) (2020)
- [Generative replay with feedback connections as a general strategy for continual learning](https://mlsanity.com/paper/1809.10635/) (2018)
- [Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures](https://mlsanity.com/paper/1910.13312/) (2019)
- [Initial Classifier Weights Replay for Memoryless Class Incremental Learning](https://mlsanity.com/paper/2008.13710/) (2020)
- [Zero-Bias Deep Learning for Accurate Identification of Internet of Things (IoT) Devices](https://mlsanity.com/paper/2009.02267/) (2020)
- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [RF-PUF: Enhancing IoT Security through Authentication of Wireless Nodes using In-situ Machine Learning](https://mlsanity.com/paper/1805.01374/) (2018)
- [Transmitter Classification With Supervised Deep Learning](https://mlsanity.com/paper/1905.07923/) (2019)
- [Radio Frequency Fingerprint Identification Based on Denoising Autoencoders](https://mlsanity.com/paper/1907.08809/) (2019)

## Built on by (most cited first, 9 of 9)

- [Exploring the Intersection between Neural Architecture Search and Continual Learning](https://mlsanity.com/paper/2206.05625/) (2022)
- [Zero-bias Deep Neural Network for Quickest RF Signal Surveillance](https://mlsanity.com/paper/2110.05797/) (2021)
- [Overcoming Domain Drift in Online Continual Learning](https://mlsanity.com/paper/2405.09133/) (2024)
- [Revolution of Wireless Signal Recognition for 6G: Recent Advances, Challenges and Future Directions](https://mlsanity.com/paper/2503.08091/) (2025)
- [NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing](https://mlsanity.com/paper/2407.20281/) (2024)
- [Dynamic Online Modulation Recognition using Incremental Learning](https://mlsanity.com/paper/2312.04718/) (2023)
- [CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection](https://mlsanity.com/paper/2504.01882/) (2025)
- [Explainable Machine Learning for Cyberattack Identification from Traffic Flows](https://mlsanity.com/paper/2505.01488/) (2025)
- [Machine Learning for Cyber-Attack Identification from Traffic Flows](https://mlsanity.com/paper/2505.01489/) (2025)

## Abstract (arXiv)

Deep Learning (DL) has been utilized pervasively in the Internet of Things (IoT). One typical application of DL in IoT is device identification from wireless signals, namely Non-cryptographic Device Identification (NDI). However, learning components in NDI systems have to evolve to adapt to operational variations, such a paradigm is termed as Incremental Learning (IL). Various IL algorithms have been proposed and many of them require dedicated space to store the increasing amount of historical data, and therefore, they are not suitable for IoT or mobile applications. However, conventional IL schemes can not provide satisfying performance when historical data are not available. In this paper, we address the IL problem in NDI from a new perspective, firstly, we provide a new metric to measure the degree of topological maturity of DNN models from the degree of conflict of class-specific fingerprints. We discover that an important cause for performance degradation in IL enabled NDI is owing to the conflict of devices' fingerprints. Second, we also show that the conventional IL schemes can lead to low topological maturity of DNN models in NDI systems. Thirdly, we propose a new Channel Separation Enabled Incremental Learning (CSIL) scheme without using historical data, in which our strategy can automatically separate devices' fingerprints in different learning stages and avoid potential conflict. Finally, We evaluated the effectiveness of the proposed framework using real data from ADS-B (Automatic Dependent Surveillance-Broadcast), an application of IoT in aviation. The proposed framework has the potential to be applied to accurate identification of IoT devices in a variety of IoT applications and services. Data and code available at IEEE Dataport (DOI: 10.21227/1bxc-ke87) and urlhttps://github.com/pcwhy/CSIL