# A Novel Online Incremental Learning Intrusion Prevention System

arXiv: https://arxiv.org/abs/2109.09530 · HTML: https://mlsanity.com/paper/2109.09530/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 27 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Built on by (most cited first, 3 of 3)

- [Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision](https://mlsanity.com/paper/2312.06229/) (2023)
- [IDPS Signature Classification with a Reject Option and the Incorporation of Expert Knowledge](https://mlsanity.com/paper/2207.10797/) (2022)
- [Candidate Comparability Before Promotion: Conditional Validation in Adaptive Network Intrusion Detection](https://mlsanity.com/paper/2609.04388/) (2026)

## Abstract (arXiv)

Attack vectors are continuously evolving in order to evade Intrusion Detection systems. Internet of Things (IoT) environments, while beneficial for the IT ecosystem, suffer from inherent hardware limitations, which restrict their ability to implement comprehensive security measures and increase their exposure to vulnerability attacks. This paper proposes a novel Network Intrusion Prevention System that utilises a SelfOrganizing Incremental Neural Network along with a Support Vector Machine. Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy. Based on our experimental results with the NSL KDD dataset, the proposed framework can achieve on-line updated incremental learning, making it suitable for efficient and scalable industrial applications.