A Novel Online Incremental Learning Intrusion Prevention System
This provides a security solution for IoT environments, addressing incremental learning needs for scalable industrial applications.
The paper tackles the problem of evolving attack vectors and hardware limitations in IoT environments by proposing a novel Network Intrusion Prevention System that uses a Self-Organizing Incremental Neural Network with a Support Vector Machine, achieving real-time mitigation of known and unknown attacks with high accuracy based on experiments with the NSL KDD dataset.
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.