Time of Flight and Fingerprinting Based Methods for Wireless Rogue Device Detection

Existing network detection techniques rely on SSIDs, network patterns or MAC addresses of genuine wireless devices to identify malicious attacks on the network. However, these device characteristics can be manipulated posing a security threat to information integrity, lowering detection accuracy, and weakening device protection. This research study focuses on empirical analysis to elaborate the relationship between received signal strength (RSSI) and distance; investigates methods to detect rogue devices and access points on Wi-Fi networks using network traffic analysis and fingerprint identification methods. In this paper, we conducted three experiments to evaluate the performance of RSSI and clock skews as features to detect rogue devices for indoor and outdoor locations. Results from the experiments suggest different devices connected to the same access point can be detected (p \u3c 0.05) using RSSI values. However, the magnitude of the difference was not consistent as devices were placed further from the same access point. Therefore, an optimal distance for maximizing the detection rate requires further examination. The random forest classifier provided the best performance with a mean accuracy of 79% across all distances. Our experiment on clock skew shows improved accuracy in using beacon timestamps to detect rogue APs on the network

Sistema de localização por luz visível para ambientes interiores baseado em um estimador de Iluminâncias

This work proposes a technique for indoor localization based on Visible Light Communication (VLC). The strategy relies on using lighting LED luminaires whose luminous flux is modulated at different frequencies. A light sensor is then used to gather the illuminance signal used as input for a previously trained Artificial Neural Network (ANN) to estimate the position of the sensor. The work’s main contribution is that the training procedure of the ANN is performed by using an illuminance estimator based on the lighting distribution of the luminaires, which is obtained through the .IES file provided by the luminaire manufacturer, which is the same file used for lighting designs. Therefore, by using the environment’s characteristics and the .IES file, the proposed system can provide data to train accurately the ANN used in the positioning method without the need to collect data from the environment. The results attested to the satisfactory performance of the proposed localization technique as it produced a mean distance error inferior to 1.16cm in a 3m × 3m × 3m environment and inferior to 3.83cm in a 3m × 1m × 3m environment, considering 0%, 30% and 70% walls reflectance levels.Este trabalho propõe uma técnica de localização interna baseada em Localização por Luz Visível (Visible Light Communication - VLC). A estratégia é baseada na utilização de luminárias de LED cujo fluxo luminoso é modulado em diferentes frequências. Um sensor de luz é então usado para coletar o sinal de iluminância recebido, o qual é usado como entrada para uma Rede Neural Artificial (RNA) previamente treinada para estimar a posição do sensor. A principal contribuição do trabalho é que o procedimento de treinamento da RNA é realizado utilizando um estimador de iluminância baseado na distribuição luminosa das luminárias, que é obtido através do arquivo .IES fornecido pelo fabricante da luminária, que é o mesmo arquivo utilizado para projetos de iluminação. Portanto, utilizando as características do ambiente e o arquivo .IES, o sistema proposto pode fornecer dados para treinar com precisão a RNA utilizada no método de posicionamento sem a necessidade de coletar dados do ambiente. Os resultados atestam o desempenho satisfatório da técnica de localização proposta ao produzir um erro médio de distância inferior a 1,16cm em um ambiente de 3m × 3m × 3m e inferior a 3,83cm em um ambiente de 3m × 1m × 3m, considerando níveis de refletância nas paredes de 0%, 30% e 70%.FAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerai

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots