IPv6 over Low-powered Wireless Personal Area Networks (6LoWPAN) have grown in importance in recent years, with the Routing Protocol for Low Power and Lossy Networks (RPL) emerging as a major enabler. However, RPL can be subject to attack, with severe consequences. Most proposed IDSs have been limited to specific RPL attacks and typically assume a stationary environment. In this article, we propose the first adaptive hybrid IDS to efficiently detect and identify a wide range of RPL attacks (including DIO Suppression, Increase Rank, and Worst Parent attacks, which have been overlooked in the literature) in evolving data environments. We apply our framework to networks under various levels of node mobility and maliciousness. We experiment with several incremental machine learning (ML) approaches and various 'concept-drift detection' mechanisms (e.g. ADWIN, DDM, and EDDM) to determine the best underlying settings for the proposed scheme.

翻译：近年来,关于低功率无线个人区域网络(6LoWPAN)的IPv6在低功率无线个人区域网络(6LoWPAN)上的重要性日益增强,《低功率和损失网络(RPL)运行协议》正在成为主要的推进器,然而,RPL可能会受到攻击,并产生严重后果。大多数拟议的IDS都局限于具体的RPL攻击,通常假定一个固定的环境。在本条中,我们建议采用第一个适应性混合ISDS,以便在不断变化的数据环境中有效探测和确定各种RPL攻击(包括DI禁止、增加等级和最坏父母攻击,这些攻击在文献中被忽视)的范围。我们把我们的框架应用到处于不同节点移动和恶意程度的网络中。我们尝试了几种渐进式机器学习(ML)方法和各种“概念驱动探测”机制(例如ADWIN、DDM和EDDDDDM),以确定拟议计划的最佳基础环境。