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

TL;DR

AOC-IDS is an innovative online intrusion detection system that uses contrastive learning and autonomous modules to adapt continuously to evolving IoT cyber threats, reducing manual effort and outperforming existing solutions.

Contribution

The paper introduces AOC-IDS, a fully online, autonomous IDS employing contrastive learning and pseudo-labeling, enabling continuous adaptation without human intervention.

Findings

Outperforms state-of-the-art solutions on NSL-KDD and UNSW-NB15 datasets.

Demonstrates high adaptability to dynamic IoT environments.

Reduces manual labeling effort through pseudo-labeling.

Abstract

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.