Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People

[EN] Internet of Things (IoT) is becoming highly supportive in innovative technological solutions for assisting impaired people. Some of these IoT solutions are still in a prototyping phase ignoring possible attacks and the corresponding security defenses. This article proposes a learning-based approach for defending against perception-layer attacks performed on specific sensor types in smart furniture for impaired people. This approach is based on the analysis of time series by means of dynamic time warping algorithm for calculating similarity and a novel detector for identifying anomalies. This approach has been illustrated by defending against simulated perception-layer magnetic attacks on a smart cupboard with door magnetic sensors. The results show the performance of the proposed approach for properly identifying these attacks. In particular, these results advocate an accuracy about 95.5% per day.This work was supported in part by the research project Utilisation of IoT and Sensors in Smart Cities for Improving Quality of Life of Impaired People under Grant 52-2020, in part by the Ciudades Inteligentes Totalmente Integrales, Eficientes Y Sotenibles (CITIES) funded by the Programa Iberoamericano de Ciencia y Tecnologia para el Desarrollo (CYTED) under Grant 518RT0558, in part by the Diseno Colaborativo Para La Promocion Del Bienestar En Ciudades Inteligentes Inclusivas under Grant TIN2017-88327-R funded by the Spanish Council of Science, Innovation and Universities from the Spanish Government, and in part by the Ministerio de Economia y Competitividad in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento under Grant TIN2017-84802-C2-1-P.Nasralla, MM.; García-Magariño, I.; Lloret, J. (2020). Defenses Against Perception-Layer Attacks on IoT Smart Furniture for Impaired People. IEEE Access. 8:119795-119805. https://doi.org/10.1109/ACCESS.2020.3004814S119795119805