Toward Predicting Secure Environments for Wearable Devices

Wearable devices have become more common for the average consumer. As devices need to operate with low power, many devices use simplified security measures to secure the data during transmission. While Bluetooth, the primary method of communication, includes certain security measures as part of the format, they are insufficient to fully secure the connection and the data transmitted. Users must be made aware of the potential security threats to the information communicated by the wearable, as well as be empowered and engaged to protect it. In this paper, we propose a method of identifying insecure environments through crowdsourced data, allowing wearable consumers to deploy an application on their base system (e.g., a smart phone) that alerts when in the presence of a security threat. We examine two different machine learning methods for classifying the environment and interacting with the users, as well as evaluating the potential uses for both algorithms

A Test Environment for Wireless Hacking in Domestic IoT Scenarios

Security is gaining importance in the daily life of every citizen. The advent of Internet of Things devices in our lives is changing our conception of being connected through a single device to a multiple connection in which the centre of connection is becoming the devices themselves. This conveys the attack vector for a potential attacker is exponentially increased. This paper presents how the concatenation of several attacks on communication protocols (WiFi, Bluetooth LE, GPS, 433 Mhz and NFC) can lead to undesired situations in a domestic environment. A comprehensive analysis of the protocols with the identification of their weaknesses is provided. Some relevant aspects of the whole attacking procedure have been presented to provide some relevant tips and countermeasures.This work has been partially supported by the Spanish Ministry of Science and Innovation through the SecureEDGE project (PID2019-110565RB-I00), and by the by the Andalusian FEDER 2014-2020 Program through the SAVE project (PY18-3724). // Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. // Funding for open access charge: Universidad de Málaga / CBU

Inferences from Interactions with Smart Devices: Security Leaks and Defenses

We unlock our smart devices such as smartphone several times every day using a pin, password, or graphical pattern if the device is secured by one. The scope and usage of smart devices\u27 are expanding day by day in our everyday life and hence the need to make them more secure. In the near future, we may need to authenticate ourselves on emerging smart devices such as electronic doors, exercise equipment, power tools, medical devices, and smart TV remote control. While recent research focuses on developing new behavior-based methods to authenticate these smart devices, pin and password still remain primary methods to authenticate a user on a device. Although the recent research exposes the observation-based vulnerabilities, the popular belief is that the direct observation attacks can be thwarted by simple methods that obscure the attacker\u27s view of the input console (or screen). In this dissertation, we study the users\u27 hand movement pattern while they type on their smart devices. The study concentrates on the following two factors; (1) finding security leaks from the observed hand movement patterns (we showcase that the user\u27s hand movement on its own reveals the user\u27s sensitive information) and (2) developing methods to build lightweight, easy to use, and more secure authentication system. The users\u27 hand movement patterns were captured through video camcorder and inbuilt motion sensors such as gyroscope and accelerometer in the user\u27s device