Personal Bluetooth Low Energy (BLE) trackers such as AirTag help locate lost items but can be misused for stalking. Research indicates that BLE trackers can be identified through their transmitted packets, thus offering potential for machine learning (ML) solutions. However, current packet datasets lack the scale and diversity needed for real-world applicability. This paper presents an open-source $\mathbf{2 0 0}$-hour BLE advertisement packet dataset focused on personal tags, enabling future ML-based device detection approaches. Additionally, introduces the first large-scale BLE data preprocessing framework for efficient and modular BLE packet preprocessing. The Framework is showcased on the dataset, demonstrating feature extraction, labelling, and dynamic plotting. This paper lays the groundwork for IoT device detection in high-density, heterogeneous environments, enabling future advances in BLE device classification.

Bluetooth Low Energy (BLE) is widely used in devices like smartphones and personal trackers, but also raises serious privacy risks, especially related to stalking. Machine Learning (ML)-based methods for detecting BLE trackers across vendors show promise, yet are limited by the scarcity and variability of BLE advertisement packets, which hinders model performance. This paper addresses this limitation by introducing the first publicly available, open-source tool for generating synthetic BLE advertisement packets using a Markov model. Designed for structured time-series data, the model can produce all valid BLE packet permutations, addressing a key data gap for research and training. As a case study, synthetic Samsung SmartTag (nearby) packets are used to augment training data, resulting in a 37% increase in median prediction confidence level in real-world evaluations.

Bluetooth Low Energy (BLE)-based trackers have become increasingly widespread due to their affordability, energy efficiency, and integration into Crowd-Sourced Finding Networks (CFNs). CFNs, such as Apple’s Find My system, leverage vast user bases to enable device location even without direct Internet connectivity. However, the expansion of such technologies has raised concerns about security and potential misuse, particularly for unauthorized tracking.This paper investigates whether Received Signal Strength Indication (RSSI) data, when combined with Machine Learning (ML) techniques, can enhance the detection and protection mechanisms for individuals targeted by such misuse. A dataset comprising 13,353 labeled entries was collected using Apple AirTags to simulate various tracking scenarios and used to train and evaluate multiple classification models. Among these, a Decision Tree classifier demonstrated a strong balance between accuracy, F1-score, and overfitting resilience, achieving an accuracy of 85.35%. The model was subsequently integrated into HomeScout, marking a promising step toward proactive misuse mitigation in BLE-tracking ecosystems.

The rise of the Internet-of-Things (IoT) and smart homes has resulted in the increased use of ZigBee as the communication protocol of choice in home networks, giving ample opportunity for network monitoring and user profiling, as a consequence, raising a major privacy concern. Yet, there has been little exploration of the extractable information solely from network packets, particularly Philips Hue packets.Especially as, to the authors’ knowledge, there have been no studies examining whether a single network key provides enough generalization to extract data from other unknown ZigBee networks. To address this gap, this paper proposes StealthProfiler, a passive and real-time Proof-of-Concept (PoC) tool designed to identify, classify, and extract devices and events within a Philips Hue network.As a result, the tool was successfully used to extract network events from encrypted Zigbee networks, achieving an accuracy of approximately 94% in identifying devices and events within the network without decrypting network traffic.

Security management of Internet-of-Things (IoT) infrastructures encompassing the entire lifecycle of products and their continuous certification are fundamental functions to guarantee a high level of security. This paper demonstrates a DLT-based platform enabling secure inventorying and management of IoT devices and sensors distributed across multiple stakeholders. This work makes use of the Hyperledger framework, Decentralized Identifiers (DID), and Manufacturer Usage Description (MUD) files for IoT device inventorying and the identification of critical software updates.

With the growing popularity of Internet-of-Things (IoT) and the smart home market, third party data stores and remote cloud environments, security and privacy of personal data is the central problem. Since the existing Privacy Enhancing Technologies (PET) have yet to provide a lightweight solution to safeguard privacy within smart homes, this paper presents the Security and Home Integration Framework for IoT (SHIFT). SHIFT leverages an open-source architecture to integrate existing tools, including home assistants and network monitors, into a unified framework designed to monitor and control smart home devices through security policies. The proof-ofconcept prototype for monitoring and controlling smart home device communications using user-defined policies successfully monitors device activity, processes data, and enforces user policies in a live smart home environment. SHIFT gives users control over their devices and data while balancing convenience with privacy and security.

Credentials must be revoked on time to avoid legal issues, information misuse, and potential economic damage. A cryptographic accumulator is the most efficient data structure to record whether a credential is valid. However, credential holders must update their verification proof, or witness whenever a credential is revoked in the system. The witness update is expensive and not feasible for applications with limited computational power, such as mobile phones. We propose a credential revocation management framework that removes the need for witness updates, while still utilising the benefits of the cryptographic accumulator. We use three accumulators, the notion of epoch and replication of accumulator transition from one state to another for verification. The evaluation shows our method is efficient and suitable for credential holders with limited computing resources.