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.

翻译：物联网环境虽然对信息技术生态系统有利,但却受到固有的硬件限制,限制了它们实施全面安全措施的能力,并增加了它们遭受脆弱性攻击的机会。本文件提议建立一个新型的网络入侵预防系统,利用一个自我组织递增神经网络和一个辅助矢量机。由于其结构,拟议系统提供了一种不依赖签名或规则的安保解决方案,并且能够以高精确度实时减少已知和未知的袭击。根据我们利用NSL KDD数据集的实验结果,拟议框架可以实现在线更新的渐进学习,使之适合高效和可扩展的工业应用。