# Online Self-Supervised Deep Learning for Intrusion Detection Systems

arXiv: https://arxiv.org/abs/2306.13030 · HTML: https://mlsanity.com/paper/2306.13030/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 71 references that are in the map.
- First posted on arXiv 22 Jun 2023.

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

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [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)
- [Self-Supervised Learning for Recommender Systems: A Survey](https://mlsanity.com/paper/2203.15876/) (2022)
- [D"IoT: A Federated Self-learning Anomaly Detection System for IoT](https://mlsanity.com/paper/1804.07474/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks](https://mlsanity.com/paper/2207.06819/) (2022)
- [Machine Learning DDoS Detection for Consumer Internet of Things Devices](https://mlsanity.com/paper/1804.04159/) (2018)
- [An Automated and Comprehensive Framework for IoT Botnet Detection and Analysis (IoT-BDA)](https://mlsanity.com/paper/2105.11061/) (2021)
- [Early Detection Of Mirai-Like IoT Bots In Large-Scale Networks Through Sub-Sampled Packet Traffic Analysis](https://mlsanity.com/paper/1901.04805/) (2019)
- [The Impact of DoS Attacks onResource-constrained IoT Devices:A Study on the Mirai Attack](https://mlsanity.com/paper/2104.09041/) (2021)

## Built on by (most cited first, 1 of 1)

- [Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection](https://mlsanity.com/paper/2403.15509/) (2024)

## Abstract (arXiv)

This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed framework analyzes and labels incoming traffic packets based only on the decisions of the IDS itself using an Auto-Associative Deep Random Neural Network, and on an online estimate of its statistically measured trustworthiness. The SSID framework enables IDS to adapt rapidly to time-varying characteristics of the network traffic, and eliminates the need for offline data collection. This approach avoids human errors in data labeling, and human labor and computational costs of model training and data collection. The approach is experimentally evaluated on public datasets and compared with well-known {machine learning and deep learning} models, showing that this SSID framework is very useful and advantageous as an accurate and online learning DL-based IDS for IoT systems.