# Joint Semantic Transfer Network for IoT Intrusion Detection

arXiv: https://arxiv.org/abs/2210.15911 · HTML: https://mlsanity.com/paper/2210.15911/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 58 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Semantic Concentration for Domain Adaptation](https://mlsanity.com/paper/2108.05720/) (2021)
- [Generalized Domain Conditioned Adaptation Network](https://mlsanity.com/paper/2103.12339/) (2021)
- [Simultaneous Semantic Alignment Network for Heterogeneous Domain Adaptation](https://mlsanity.com/paper/2008.01677/) (2020)
- [Efficient Learning of Domain-invariant Image Representations](https://mlsanity.com/paper/1301.3224/) (2013)
- [Heterogeneous Domain Adaptation via Soft Transfer Network](https://mlsanity.com/paper/1908.10552/) (2019)
- [Multi-source Heterogeneous Domain Adaptation with Conditional Weighting Adversarial Network](https://mlsanity.com/paper/2008.02714/) (2020)
- [AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks](https://mlsanity.com/paper/1806.02566/) (2018)
- [Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking](https://mlsanity.com/paper/1803.05022/) (2018)

## 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)
- [Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach](https://mlsanity.com/paper/2301.09801/) (2023)
- [Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection](https://mlsanity.com/paper/2303.14317/) (2023)
- [Open Set Dandelion Network for IoT Intrusion Detection](https://mlsanity.com/paper/2311.11249/) (2023)

## Abstract (arXiv)

In this paper, we propose a Joint Semantic Transfer Network (JSTN) towards effective intrusion detection for large-scale scarcely labelled IoT domain. As a multi-source heterogeneous domain adaptation (MS-HDA) method, the JSTN integrates a knowledge rich network intrusion (NI) domain and another small-scale IoT intrusion (II) domain as source domains, and preserves intrinsic semantic properties to assist target II domain intrusion detection. The JSTN jointly transfers the following three semantics to learn a domain-invariant and discriminative feature representation. The scenario semantic endows source NI and II domain with characteristics from each other to ease the knowledge transfer process via a confused domain discriminator and categorical distribution knowledge preservation. It also reduces the source-target discrepancy to make the shared feature space domain-invariant. Meanwhile, the weighted implicit semantic transfer boosts discriminability via a fine-grained knowledge preservation, which transfers the source categorical distribution to the target domain. The source-target divergence guides the importance weighting during knowledge preservation to reflect the degree of knowledge learning. Additionally, the hierarchical explicit semantic alignment performs centroid-level and representative-level alignment with the help of a geometric similarity-aware pseudo-label refiner, which exploits the value of unlabelled target II domain and explicitly aligns feature representations from a global and local perspective in a concentrated manner. Comprehensive experiments on various tasks verify the superiority of the JSTN against state-of-the-art comparing methods, on average a 10.3% of accuracy boost is achieved. The statistical soundness of each constituting component and the computational efficiency are also verified.