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A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

机译：基于机器学习的移动互联网入侵检测系统

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Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.
机译：入侵检测系统在检测诋毁网络性能的恶意活动方面发挥着关键作用。移动adhoc网络（船只）和无线传感器网络（WSNS）是一种无线网络的形式，可以在没有任何基础设施的情况下传输数据的无线网络。最近出现了更多新的网络范式，即事物互联网（物联网），可以被视为上述范式的超级赛。它们的分布性质和有限的资源可用，为这些网络提供安全性带来了相当大的挑战。对可以适应这种挑战的入侵检测系统（IDS）的需求具有极大的意义。以前，我们提出了一种具有两层检测层的基于交叉层的ID。它采用了一种基于正确分类的实例（CCI）的可变性的启发式方法，我们将其称为波动的累积量（AMOF）。目前的提议ID由两个阶段组成;阶段通过专用嗅探器（DSS）收集数据，并生成以周期性方式发送到超级节点（SN）的CCI，并且在阶段2中，SN执行来自不同DSS的CCCIS的线性回归过程，以便从恶意节点中区分良性节点。在这项工作中，在网络中的不同极端场景呈现了检测表征，与两个不同移动模型的功率电平和节点速度有关：随机方式点（RWP）和Gauss Markov（GM）。工作中使用的恶意活动是黑洞和分布式拒绝服务（DDOS）攻击。对于高功率/节点速度方案，检测速率超过98％，而低功率/节点速度方案，它们跌至约90％。

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