Wi-attack: Cross-technology Impersonation Attack against iBeacon Services

iBeacon protocol is widely deployed to provide location-based services. By receiving its BLE advertisements, nearby devices can estimate the proximity to the iBeacon or calculate indoor positions. However, the open nature of these advertisements brings vulnerability to impersonation attacks. Such attacks could lead to spam, unreliable positioning, and even security breaches. In this paper, we propose Wi-attack, revealing the feasibility of using WiFi devices to conduct impersonation attacks on iBeacon services. Different from impersonation attacks using BLE compatible hardware, Wi-attack is not restricted by broadcasting intervals and is able to impersonate multiple iBeacons at the same time. Effective attacks can be launched on iBeacon services without modifications to WiFi hardware or firmware. To enable direct communication from WiFi to BLE, we use the digital emulation technique of cross technology communication. To enhance the packet reception along with its stability, we add redundant packets to eliminate cyclic prefix error entirely. The emulation provides an iBeacon packet reception rate up to 66.2%. We conduct attacks on three iBeacon services scenarios, point deployment, multilateration, and fingerprint-based localization. The evaluation results show that Wi-attack can bring an average distance error of more than 20 meters on fingerprint-based localization using only 3 APs.Comment: 9 pages; 26 figures; 2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON), 202

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

State of the Art, Trends and Future of Bluetooth Low Energy, Near Field Communication and Visible Light Communication in the Development of Smart Cities

The current social impact of new technologies has produced major changes in all areas of society, creating the concept of a smart city supported by an electronic infrastructure, telecommunications and information technology. This paper presents a review of Bluetooth Low Energy (BLE), Near Field Communication (NFC) and Visible Light Communication (VLC) and their use and influence within different areas of the development of the smart city. The document also presents a review of Big Data Solutions for the management of information and the extraction of knowledge in an environment where things are connected by an “Internet of Things” (IoT) network. Lastly, we present how these technologies can be combined together to benefit the development of the smart city

A Context-Aware System to Secure Enterprise Content: Incorporating Reliability Specifiers

The sensors of a context-aware system extract contextual information from the environment and relay that information to higher-level processes of the system so to influence the system\u2019s control decisions. However, an adversary can maliciously influence such controls indirectly by manipulating the environment in which the sensors are monitoring, thereby granting privileges the adversary would otherwise not normally have. To address such context monitoring issues, we extend CASSEC by incorporating sentience-like constructs, which enable the emulation of \u201dconfidence\u201d, into our proximity-based access control model to grant the system the ability to make more inferable decisions based on the degree of reliability of extracted contextual information. In CASSEC 2.0, we evaluate our confidence constructs by implementing two new authentication mechanisms. Co-proximity authentication employs our time-based challenge-response protocol, which leverages Bluetooth Low Energy beacons as its underlying occupancy detection technology. Biometric authentication relies on the accelerometer and fingerprint sensors to measure behavioral and physiological user features to prevent unauthorized users from using an authorized user\u2019s device. We provide a feasibility study demonstrating how confidence constructs can improve the decision engine of context-aware access control systems

Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

Indoor navigation systems based on data mining techniques in internet of things: a survey

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges