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Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection

机译:使用异常检测的COAP无线传感器网络入侵检测和预防

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摘要

It is well recognized that security will play a major role in enabling most of the applications envisioned for the Internet of Things (IoT). We must also note that most of such applications will employ sensing and actuating devices integrated with the Internet communications infrastructure and, from the minute such devices start to support end-to-end communications with external (Internet) hosts, they will be exposed to all kinds of threats and attacks. With this in mind, we propose an IDS framework for the detection and prevention of attacks in the context of Internet-integrated CoAP communication environments and, in the context of this framework, we implement and experimentally evaluate the effectiveness of anomaly-based intrusion detection, with the goal of detecting Denial of Service (DoS) attacks and attacks against the 6LoWPAN and CoAP communication protocols. From the results obtained in our experimental evaluation we observe that the proposed approach may viably protect devices against the considered attacks. We are able to achieve an accuracy of 93% considering the multi-class problem, thus when the pattern of specific intrusions is known. Considering the binary class problem, which allows us to recognize compromised devices, and though a lower accuracy of 92% is observed, a recall and an F_Measure of 98% were achieved. As far as our knowledge goes, ours is the first proposal targeting the usage of anomaly detection and prevention approaches to deal with application-layer and DoS attacks in 6LoWPAN and CoAP communication environments.
机译:很高兴认识到,安全性将发挥重要作用,使大多数应用程序设想为互联网(IOT)。还必须注意,大多数此类应用程序将采用与互联网通信基础架构集成的传感和执行设备,并且从此类设备开始支持与外部(Internet)主机的端到端通信,它们将被暴露于所有各种威胁和攻击。考虑到这一点,我们提出了一个IDS框架,用于检测和预防互联网集成的COAP通信环境中的攻击,并且在本框架的背景下,我们实施并通过实验评估基于异常的入侵检测的有效性,通过检测拒绝服务(DOS)攻击和针对6LowPAN和CAAP通信协议的攻击的目标。从我们的实验评估中获得的结果,我们观察到所提出的方法可以通过防范防范所考虑的攻击。考虑到多级问题,我们能够达到93%的准确性,从而知道特定入侵的模式。考虑到二进制类问题,这使我们能够识别受损设备,并且观察到92%的较低精度,召回和98%的F_MESURE。据我们所知,我们的是第一个针对异常检测和预防方法来处理6LowPan和Coop通信环境中的应用层和DOS攻击的第一个提案。

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