Improving compliance with bluetooth device detection

The number of devices containing Bluetooth chipsets is continuing to rise and there exists a need to stem the tidal wave of vulnerabilities brought by the Bring Your Own Device (BYOD) and Internet of Things (IoT) phenomena. With Bluetooth enabled but discovery mode turned off, auditing for Bluetooth devices, or creating an accurate Bluetooth device hardware log is limited. The software tools and hardware devices to monitor WiFi networking signals have long been a part of the security auditor’s arsenal, but similar tools for Bluetooth are bespoke, expensive, and not adopted by most security pentesters. However, this has changed with the introduction of the Ubertooth One, a low-cost and open-source platform for monitoring Bluetooth Classic signals. Using a combination of the Ubertooth One, and other high-power Bluetooth devices, an auditor should now be able to actively scan for rogue devices that may otherwise have been missed. This research examines various hardware combinations that can be used to achieve this functionality, and the possible implications from a compliance point of view, with a focus on the standards used by the Payment Card Industry Data Security Standard (PCI-DSS), and the guidelines offered by the National Institute of Standards and Technology (NIST). We compare the results of scanning with traditional Bluetooth devices as opposed to an Ubertooth/Bluetooth combination. We show how the ability to monitor a larger portion of Bluetooth traffic can highlight serious implications in the compliance landscape of many organisations and companies. We demonstrate that identifying non-discoverable devices with Bluetooth enabled is a crucial element in holistic security monitoring of threats

Voice Enabled Indoor Localisation

The ability to track objects in real time offers a wide range of beneficial applications that include safety, security and the supply chain. The problem with location based systems is that they can be inconvenient and time-consuming to locate an object. A user has to access a computer and log onto a location system to locate an object. There are several problems with current location determination interfaces. Firstly a user has to log on to a computer; this can be inconvenient and time consuming as the user may have to locate a computer (which may be in another area of a building) and then log onto the system. The user has to look at a map that is displayed on screen to see where the object is located; the problem with this is that the user could make a mistake by looking at the wrong object or the wrong area of the building. Incorporating a voice control function into the system can solve interaction problems with some location based systems. This paper provides an overview of integrating voiceXML with an indoor location positioning system to locate objects through voice commands

HABITS - a History Aware Based Indoor Tracking System

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Using Digital Footprints for a City-scale Traffic Simulation

This article introduces a micro-simulation of urban traffic flows within a large scale scenario implemented for the Greater Dublin region in Ireland. Traditionally, the data available for traffic simulations come from a population census and dedicated road surveys which only partly cover shopping, leisure or recreational trips. To account for the latter, the presented traffic modelling framework exploits the digital footprints of city inhabitants on services such as Twitter and Foursquare. We enriched the model with findings from our previous studies on geographical layout of communities in a country-wide mobile phone network to account for socially related journeys. These data-sets were used to calibrate a variant of a radiation model of spatial choice, which we introduced in order to drive individuals' decisions on trip destinations within an assigned daily activity plan. We observed that given the distribution of population, the workplace locations, a comprehensive set of urban facilities and a list of typical activity sequences of city dwellers collected within a national travel survey, the developed micro-simulation reproduces not only the journey statistics such as peak travel periods but also the traffic volumes at main road segments with surprising accuracy