Occupancy detection for building emergency management using BLE beacons

Being able to reliable estimate the occupancy of areas inside a building can prove beneficial for managing an emergency situation, as it allows for more efficient allocation of resources such as emergency personnel. In indoor environments, however, occupancy detection can be a very challenging task. A solution to this can be provided by the use of Bluetooth Low Energy (BLE) beacons installed in the building. In this work we evaluate the performance of a BLE based occupancy detection system geared towards emergency situations that take place inside buildings. The system is composed of BLE beacons installed inside the building, a mobile application installed on occupants' mobile phones and a remote control server. Our approach does not require any processing to take place on the occupants' mobile phones, since the occupancy detection is based on a classifier installed on the remote server. Our real-world experiments indicated that the system can provide high classification accuracy for different numbers of installed beacons and occupant movement patterns

Activity recognition in a home setting using off the shelf smart watch technology

Being able to detect in real-time the activity per- formed by a user in a home setting provides highly valuable context. It can allow more effective use of novel technologies in a large variety of applications, from comfort and safety to energy efficiency, remote health monitoring and assisted living. In a home setting, activity recognition has been traditionally studied based on either a large sensor network infrastructure already set up in a home, or a network of wearable sensors attached to various parts of the user’s body. We argue that both approaches suffer considerably in terms of practicality and propose instead the use of commercial off-the-shelf smart watches, already owned by the users. We test the feasibility of this approach with two different smart watches of very different capabilities, on a variety of activities performed daily in a domestic environment, from brushing teeth to preparing food. Our experimental results are encouraging, as using stan- dard Support Vector Machine based classification, the accuracy rates range between 88% and 100%, depending on the type of smart watch and the window size chosen for data segmentation

Towards real-time profiling of human attackers and bot detection

Characterising the person behind a cyber attack can be highly useful. At a practical security and forensic level, it can help profile adversaries during and after an attack, and at a theoretical level it can allow us to build improved threat models. This is, however, a challenging problem, as relevant data cannot easily be found. They are not often released publicly and may be the result of criminal investigation. Moreover, the identity of an attacker is rarely revealed in an attack. Here, we attempt a rather unusual approach. We attempt to classify the adversary as a type of human user, arguing that if it does not fit in any realistic profile of a human user, then it is probably a bot. Hence, we are working towards a system that is both a human attacker profiler and an anomaly-based bot detector. For this, we first need to build a technical system that collects relevant data in real- time. As no such information exists, we experimented with several different measurable input data and human profile characteristics, evaluating the usefulness of the former in determining the latter. We then present a case-based reason- ing approach that classifies an attacker based on the values of these metrics. For this, we use experimental data that we have previously collected and are the result of a set of cyber-attack scenarios carried out by 87 users. As a practical application, we have developed an automated profiling tool demonstrating the potential real-time use of the proposed system in a quasi-realistic setting. We discuss this approach’s ability for an adversary that has already gained access to a target system. The profile identified should tell us the characteristics of the adversary if it is human. If no profile can be identified, we argue that this is a good indication it is a bot

Bluetooth low energy based occupancy detection for emergency management

A reliable estimation of an area’s occupancy can be beneficial to a large variety of applications, and especially in relation to emergency management. For example, it can help detect areas of priority and assign emergency personnel in an efficient manner. However, occupancy detection can be a major challenge in indoor environments. A recent technology that can prove very useful in that respect is Bluetooth Low Energy (BLE), which is able to provide the location of a user using information from beacons installed in a building. Here, we evaluate BLE as the primary means of occupancy estimation in an indoor environment, using a prototype system composed of BLE beacons, a mobile application and a server. We employ three machine learning approaches (k-nearest neighbours, logistic regression and support vector machines) to determine the presence of occupants inside specific areas of an office space and we evaluate our approach in two independent experimental settings. Our experimental results indicate that combining BLE with machine learning is certainly promising as the basis for occupancy estimation

A Survey on Emergency Preparedness of EU Citizens

Population preparedness plays a crucial role in disaster management since it can help reduce the number of victims and restrict damage. Nevertheless, little work has been done at a European level towards preparing populations to learn how to cope with disasters and involving them in the disaster management process. In this paper we present the preliminary results of an on-line emergency preparedness survey circulated among EU citizens, which aims to identify and analyse people‟s behaviour in terms of preparedness, first reaction, risk awareness and willingness to engage in preparedness actions. Our preliminary analysis, based on over 1200 participants, indicates that although EU populations have a high capability for participation in emergency response, their preparedness level is low. We also found that national differences are a significant factor affecting individual preparedness behaviour and awareness of risks

Evaluating the impact of malicious spoofing attacks on Bluetooth low energy based occupancy detection systems

Occupancy detection of a building has a wide range of applications. Areas such as emergency management, home automation and building energy management can benefit from the knowledge of occupants' locations to provide better results and improve their efficiency. Bluetooth Low Energy (BLE) beacons installed inside a building are able to provide information on an occupant's location. Since, however, their operation is based on broadcasting advertisements, they are vulnerable to network security breaches. In this work, we evaluate the effect of two types of spoofing attacks on a BLE based occupancy detection system. The system is composed of BLE beacons installed inside the building, a mobile application installed on occupants’ mobile phones and a remote control server. Occupancy detection is performed by a classifier installed on the remote server. We use our real-world experimental results to evaluate the impact of these attacks on the system's operation, particularly in terms of the accuracy with which it can provide location information

Location-enhanced activity recognition in indoor environments using off the shelf smart watch technology and BLE beacons

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation

Design of an immersive online crisis preparation learning environment

This paper describes the design and development of an online immersive learning environment focused on supporting the general public in awareness of, and preparation for, crisis situations. The system developed uses the PANDORA+ training environment, and integrates prior research work carried out on eLearning and Crisis Management. Specifically, it pulls together the outputs of three European funded research projects, described in the paper, which provided the authors with a rich multimedia, immersive training environment for crisis managers, experience in the management and support of a large, distributed learning exercise through a MOOC, and extensive survey information on general population awareness of crisis responses and attitudes to crisis preparation. Based on these outputs, the authors are using the PANDORA+ training environment both as a field event support tool and as a MOOC platform, to support large-scale general public crisis preparation training

Physical indicators of cyber attacks against a rescue robot

Responding to an emergency situation is a challenging and time critical procedure. The primary goal is to save lives and this is directly related to the speed and efficiency at which help is provided to the victims. Rescue robots are able to benefit an emergency response procedure by searching for survivors, providing access to inaccessible areas and establishing an on-site communication network. This paper investigates how a cyber attack on a rescue robot can adversely affect its operation and impair an emergency response operation. The focus is on identifying physical indicators of an ongoing cyber attack, which can help to design more efficient detection and defense mechanisms. A number of experiments have been conducted on an Arduino based robot, under different cyber attack scenarios. The results show that the cyber attack’s effects have physical features that can be used in order to improve the robot’s robustness against this type of threat

Profiling cyber attackers using case-based reasoning

Computer security would arguably benefit from more information on the characteristics of the particular human attacker behind a security incident. Nevertheless, technical security mechanisms have always focused on the at- tack's characteristics rather than the attacker's. The latter is a challenging prob- lem, as relevant data cannot easily be found. We argue that the cyber traces left by a human attacker during an intrusion attempt can help towards building a profile of the particular person. To illustrate this concept, we have developed an approach using case-based reasoning that indirectly measures an attacker’s characteristics for given attack scenarios. Our results reveal that case-based rea- soning has the potential of being used to assist security and forensic investiga- tors in profiling human attackers