# AOC-IDS: Autonomous Online Framework with Contrastive Learning for Intrusion Detection

arXiv: https://arxiv.org/abs/2402.01807 · HTML: https://mlsanity.com/paper/2402.01807/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2024.

## Built on by (most cited first, 8 of 8)

- [Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection](https://mlsanity.com/paper/2412.16264/) (2024)
- [Decompose to Understand, Fuse to Detect: Frequency-Decoupled Anomaly Detection for Encrypted Network Traffic](https://mlsanity.com/paper/2605.02970/) (2026)
- [Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice](https://mlsanity.com/paper/2403.17458/) (2024)
- [A Novel Contrastive Loss for Zero-Day Network Intrusion Detection](https://mlsanity.com/paper/2601.09902/) (2026)
- [Adaptive NAD: Online and Self-adaptive Unsupervised Network Anomaly Detector](https://mlsanity.com/paper/2410.22967/) (2024)
- [Shift Detection and Adaptation for Network Intrusion Detection](https://mlsanity.com/paper/2508.15100/) (2025)
- [Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures](https://mlsanity.com/paper/2605.26166/) (2026)
- [Disentangling Multi-View Scanning in Mamba for Network Traffic Anomaly Detection](https://mlsanity.com/paper/2607.22829/) (2026)

## Abstract (arXiv)

The rapid expansion of the Internet of Things (IoT) has raised increasing concern about targeted cyber attacks. Previous research primarily focused on static Intrusion Detection Systems (IDSs), which employ offline training to safeguard IoT systems. However, such static IDSs struggle with real-world scenarios where IoT system behaviors and attack strategies can undergo rapid evolution, necessitating dynamic and adaptable IDSs. In response to this challenge, we propose AOC-IDS, a novel online IDS that features an autonomous anomaly detection module (ADM) and a labor-free online framework for continual adaptation. In order to enhance data comprehension, the ADM employs an Autoencoder (AE) with a tailored Cluster Repelling Contrastive (CRC) loss function to generate distinctive representation from limited or incrementally incoming data in the online setting. Moreover, to reduce the burden of manual labeling, our online framework leverages pseudo-labels automatically generated from the decision-making process in the ADM to facilitate periodic updates of the ADM. The elimination of human intervention for labeling and decision-making boosts the system's compatibility and adaptability in the online setting to remain synchronized with dynamic environments. Experimental validation using the NSL-KDD and UNSW-NB15 datasets demonstrates the superior performance and adaptability of AOC-IDS, surpassing the state-of-the-art solutions. The code is released at https://github.com/xinchen930/AOC-IDS.