# Effective Intrusion Detection in Highly Imbalanced IoT Networks with Lightweight S2CGAN-IDS

arXiv: https://arxiv.org/abs/2306.03707 · HTML: https://mlsanity.com/paper/2306.03707/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 56 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Learning to Compare Image Patches via Convolutional Neural Networks](https://mlsanity.com/paper/1504.03641/) (2015)
- [A Cost-Sensitive Deep Belief Network for Imbalanced Classification](https://mlsanity.com/paper/1804.10801/) (2018)
- [A Transfer Learning Approach for Network Intrusion Detection](https://mlsanity.com/paper/1909.02352/) (2019)
- [Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework](https://mlsanity.com/paper/1901.07949/) (2019)
- [I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems](https://mlsanity.com/paper/2009.10940/) (2020)

## Built on by (most cited first, 2 of 2)

- [MixGAN: A Hybrid Semi-Supervised and Generative Approach for DDoS Detection in Cloud-Integrated IoT Networks](https://mlsanity.com/paper/2508.19273/) (2025)
- [AnchorMixGAN: Anchor-Aligned Generative Semi-Supervision for DDoS Detection in Cloud-Integrated IoT Networks](https://mlsanity.com/paper/2609.32764/) (2026)

## Abstract (arXiv)

Since the advent of the Internet of Things (IoT), exchanging vast amounts of information has increased the number of security threats in networks. As a result, intrusion detection based on deep learning (DL) has been developed to achieve high throughput and high precision. Unlike general deep learning-based scenarios, IoT networks contain benign traffic far more than abnormal traffic, with some rare attacks. However, most existing studies have been focused on sacrificing the detection rate of the majority class in order to improve the detection rate of the minority class in class-imbalanced IoT networks. Although this way can reduce the false negative rate of minority classes, it both wastes resources and reduces the credibility of the intrusion detection systems. To address this issue, we propose a lightweight framework named S2CGAN-IDS. The proposed framework leverages the distribution characteristics of network traffic to expand the number of minority categories in both data space and feature space, resulting in a substantial increase in the detection rate of minority categories while simultaneously ensuring the detection precision of majority categories. To reduce the impact of sparsity on the experiments, the CICIDS2017 numeric dataset is utilized to demonstrate the effectiveness of the proposed method. The experimental results indicate that our proposed approach outperforms the superior method in both Precision and Recall, particularly with a 10.2% improvement in the F1-score.