The Internet of Things (IoT) plays an important role in the coming era of the Internet development. In addition to the convenience and opportunities it brings to us, there comes with the security issues, which could lead to the privacy leakage, it’s a threaten to the whole IoT system. To deal with the potential dangerous element hidden behind this technology, monitoring on the network would be indispensable. To develop and implements the digital network watchdog system that monitors the local network and the connected device, firstly, I surveyed the area related to the IoT attacks. The network monitor system provides basic network monitoring function, connected device tracking and monitoring function, reliable device operating function. I used the packages provided by Raspberry Pi to realize the general monitoring and transferred the captured result for further analysis. Also, I made use of SNMP and drawing tool to create graphs of different parameters in the monitoring of both network and connected devices. Then I implemented database with web service on Raspberry Pi to realize device operating. In evaluation, the system works well in general monitoring with all information provided and low lost package percentage, the graphs can provide situation of different parameters, and the respond time in the operation time of database is short. I discussed the ethical thinking and proposed the ethical thinking and future work.

This thesis explores the creation and setup of a prototype that allows users of the device to interact within an indoor real world environment and a virtual environment simultaneously using high-tech common technology. The prototype is comprised of a small mobile device such as a cellular mobile phone, Raspberry Pi computer, a battery powered handheld Pico projector, and software developed for the Android OS. The software can easily be ported to other mobile and non-mobile operating systems. The mobile device must contain accelerometer, magnetometer, and gyroscope embedded sensors as well as 802.11 wireless network chip. The prototype software implements an indoor positioning system to track the current location and orientation of the prototype device in real time. It also displays a virtual world projection upon the surfaces of the real world in relation to the prototype’s physical location and orientation. Three different orientation estimation methods were tested and compared in this thesis. Accelerometer and magnetometer based method, gyroscope based method, and a combined method using a technique called sensor fusion were implemented. A multilateration approach was used for location estimation. Location estimates were calculated from the measured received signal strength of multiple 802.11 wireless network access points. The location of all wireless access points were known and fixed. Received signal strength data was converted to meters using a log distance propagation model, and tests were conducted to compare actual distance with converted distance. Tests were also conducted to compare multilateration estimates from unfiltered or raw RSS and filtered RSS data using a Kalman filter.

[pt] Corpos estranhos ferromagnéticos inseridos acidentalmente em pacientes geralmente precisam de remoção cirúrgica. Os métodos convencionalmente empregados para localizar corpos estranhos são frequentemente ineficazes devido à baixa precisão na determinação da posição do objeto e representam riscos decorrentes da exposição da equipe médica e dos pacientes à radiação ionizante durante procedimentos de longa duração. Novos métodos utilizando sensores SQUID têm obtido sucesso na localização de corpos estranhos de forma inócua e não invasiva, mas têm a desvantagem de apresentar alto custo e baixa portabilidade. Este trabalho faz parte de pesquisas que buscam trazer maior portabilidade e baixo custo na localização de corpos estranhos no corpo humano utilizando sensores GMI e GMR. O objetivo principal deste trabalho é avaliar e aplicar o uso de Aprendizado Profundo para a localização de corpos estranhos ferromagnéticos no corpo humano utilizando um dispositivo portátil e manual baseado em magnetômetro GMR, incluindo o rastreamento da posição e orientação deste dispositivo a partir de imagens de padrões conhecidos obtidas por uma câmera integrada ao dispositivo e a solução do problema inverso magnético a partir do mapeamento magnético obtido. As técnicas apresentadas se mostraram capazes de rastrear o dispositivo com boa precisão e detectar a localização do corpo estranho com resultados semelhantes ou melhores do que os obtidos em trabalhos anteriores, dependendo do parâmetro. Os resultados obtidos são promissores como base para desenvolvimentos futuros. / [en] Ferromagnetic foreign bodies accidentally inserted in patients usually need to be surgically removed. The methods conventionally employed for locating foreign bodies are often ineffective due to the low accuracy in determining the position of the object and pose risks arising from the exposure of medical staff and patients to ionizing radiation during long-term procedures. New methods using SQUID sensors successfully located foreign bodies in an innocuous and noninvasive way, but they have the drawback of presenting high cost and low portability. This work is part of new research that seeks to bring greater portability and low cost in locating foreign bodies in the human body using GMI and GMR sensors. The main objective of this work is to evaluate and apply the use of Deep Learning in the development of a portable and manual device based on a GMR sensor, including position tracking and orientation of this device from images of known patterns obtained by a camera integrated to the device and the solution of the inverse magnetic problem from the obtained magnetic mapping. The techniques presented are capable of tracking the device with good accuracy and detecting the localization of the foreign body with similar or better results than those obtained in previous works, depending on the parameter. The results obtained are promising as a basis for future developments.

<p>Bluetooth is the de facto standard for short-range wireless communications. Besides Bluetooth Classic (BC), Bluetooth also consists of Bluetooth Low Energy (BLE) and Bluetooth Mesh (Mesh), two relatively new protocols, paving the way for its domination in the era of IoT and 5G. Meanwhile, attacks against Bluetooth, such as BlueBorne, BleedingBit, KNOB, BIAS, and BThack, have been booming in the past few years, impacting the security and privacy of billions of devices. These attacks exploit both design issues in the Bluetooth specification and vulnerabilities of its implementations, allowing for privilege escalation, remote code execution, breaking cryptography, spoofing, device tracking, etc.</p> <p><br></p> <p>To secure Bluetooth, researchers have proposed different approaches for both Bluetooth specification (e.g., formal analysis) and implementation (e.g., fuzzing). However, existing analyses of the Bluetooth specification and implementations are either done manually, or the automatic approaches only cover a small part of the targets. As a consequence, current research is far from complete in securing Bluetooth.</p> <p><br></p> <p>Therefore, in this dissertation, we propose the following research to provide missing pieces in prior research toward completing Bluetooth security research in terms of both Bluetooth specification and implementations. (i) For Bluetooth security at the specification level, we start from one protocol in Bluetooth, BLE, and focus on the previously unexplored reconnection procedure of two paired BLE devices. We conduct a formal analysis of this procedure defined in the BLE specification to provide security guarantees and identify new vulnerabilities that allow spoofing attacks. (ii) Besides BLE, we then formally verify other security-critical protocols in all Bluetooth protocols (BC, BLE, and Mesh). We provide a comprehensive formal analysis by covering the aspects that prior research fails to include (i.e., all possible combinations of protocols and protocol configurations) and considering a more realistic attacker model (i.e., semi-compromised device). With this model, we are able to rediscover five known vulnerabilities and reveal two new issues that affect BC/BLE dual-stack devices and Mesh devices, respectively. (iii) In addition to the formal analysis of specification security, we propose and build a comprehensive formal model to analyze Bluetooth privacy (i.e., device untraceability) at the specification level. In this model, we convert device untraceability into a reachability problem so that it can be verified using existing tools without introducing false results. We discover four new issues allowed in the specification that can lead to eight device tracking attacks. We also evaluate these attacks on 13 Bluetooth implementations and find that all of them are affected by at least two issues. (iv) At the implementation level, we improve Bluetooth security by debloating (i.e., removing code) Bluetooth stack implementations, which differs from prior automatic approaches, such as fuzzing. We keep only the code of needed functionality by a user and minimize their Bluetooth attack surface by removing unneeded Bluetooth features in both the host stack code and the firmware. Through debloating, we can remove 20 known CVEs and prevent a wide range of attacks again Bluetooth. With the research presented in this thesis, we improve Bluetooth security and privacy at both the specification and implementation levels.</p>

Táto práca sa zaoberá problematikou identifikácie sieťových zariadení na základe ich chovania v sieti. S neustále sa zvyšujúcim počtom zariadení na sieti je neustále dôležitejšia schopnosť identifikovať zariadenia z bezpečnostných dôvodov. Táto práca ďalej pojednáva o základoch počítačových sietí a metódach, ktoré boli využívané v minulosti na identifikáciu sieťových zariadení. Následne sú popísané algoritmy využívané v strojovom učení a taktiež sú popísané ich výhody i nevýhody. Nakoniec, táto práca otestuje dva tradičné algorithmy strojového učenia a navrhuje dva nové prístupy na identifikáciu sieťových zariadení. Výsledný navrhovaný algoritmus v tejto práci dosahuje 89% presnosť identifikácii sieťových zariadení na reálnej dátovej sade s viac ako 10000 zariadeniami.