False Data Detection for Fog and Internet of Things Networks

Romano Fantacci, Francesca Nizzi, Tommaso Pecorella, Laura Pierucci, Manuel Roveri
<span title="2019-09-29">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
The Internet of Things (IoT) context brings new security issues due to billions of smart end-devices both interconnected in wireless networks and connected to the Internet by using different technologies. In this paper, we propose an attack-detection method, named Data Intrusion Detection System (DataIDS), based on real-time data analysis. As end devices are mainly resource constrained, Fog Computing (FC) is introduced to implement the DataIDS. FC increases storage, computation capabilities,
more &raquo; ... processing capabilities, allowing it to detect promptly an attack with respect to security solutions on the Cloud. This paper also considers an attack tree to model threats and vulnerabilities of Fog/IoT scenarios with heterogeneous devices and suggests countermeasure costs. We verify the performance of the proposed DataIDS, implementing a testbed with several devices that measure different physical quantities and by using standard data-gathering protocols.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19194235">doi:10.3390/s19194235</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f5igtthkore4biftdgiadfbonq">fatcat:f5igtthkore4biftdgiadfbonq</a> </span>
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