论文标题
6Lowpan的自适应混合异质ID
Adaptive Hybrid Heterogeneous IDS for 6LoWPAN
论文作者
论文摘要
近年来，低功率无线个人区域网络（6LOWPAN）的IPv6变得重要性，其低功率和有损网络的路由协议（RPL）作为主要的推动者。但是，RPL可能会受到攻击，并带来严重的后果。大多数建议的IDS仅限于特定的RPL攻击，通常假设一个固定环境。在本文中，我们提出了第一个自适应混合ID，以有效地检测并确定广泛的RPL攻击（包括DIO抑制，增加等级和最差的父母攻击，这些攻击在文献中被忽略了）。我们将框架应用于各个级别的节点移动性和恶意性的网络。我们尝试了几种增量机器学习（ML）方法和各种“概念档检测”机制（例如Adwin，DDM和EDDM），以确定所提出的方案的最佳基础设置。
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.
