Can Evil IoT Twins Be Identified? Now Yes, a Hardware Behavioral Fingerprinting Methodology

The connectivity and resource-constrained nature of IoT, and in particular single-board devices, opens up to cybersecurity concerns affecting the Industrial Internet of Things (IIoT). One of the most important is the presence of evil IoT twins. Evil IoT twins are malicious devices, with identical hardware and software configurations to authorized ones, that can provoke sensitive information leakages, data poisoning, or privilege escalation in industrial scenarios. Combining behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques is a promising solution to identify evil IoT twins by detecting minor performance differences generated by imperfections in manufacturing. However, existing solutions are not suitable for single-board devices because they do not consider their hardware and software limitations, underestimate critical aspects during the identification performance evaluation, and do not explore the potential of ML/DL techniques. Moreover, there is a dramatic lack of work explaining essential aspects to considering during the identification of identical devices. This work proposes an ML/DL-oriented methodology that uses behavioral fingerprinting to identify identical single-board devices. The methodology leverages the different built-in components of the system, comparing their internal behavior with each other to detect variations that occurred in manufacturing processes. The validation has been performed in a real environment composed of identical Raspberry Pi 4 Model B devices, achieving the identification for all devices by setting a 50% threshold in the evaluation process. Finally, a discussion compares the proposed solution with related work and provides important lessons learned and limitations

Deep Learning-based Transmitter identification on the physical layer

An essential part of most wireless communications systems is the identification of a transmitter by a receiver. Being able to identify a transmitter at the physical layer gives context to the communication itself, but is also an important building block for more advanced techniques such as physical layer security. It can also be used to reduce overhea

Latency performance modelling in hyperledger fabric blockchain: Challenges and directions with an IoT perspective

Blockchain is a decentralized and distributed ledger technology that enables secure and transparent recording of transactions across multiple participants. Hyperledger Fabric (HLF), a permissioned blockchain, enhances performance through its modular design and pluggable consensus. However, integrating HLF with enterprise applications introduces latency challenges. Researchers have proposed numerous latency performance modelling techniques to address this issue. These studies contribute to a deeper understanding of HLF's latency by employing various modelling approaches and exploring techniques to improve network latency. However, existing HLF latency modelling studies lack an analysis of how these research efforts apply to specific use cases. This paper examines existing research on latency performance modelling in HLF and the challenges of applying these models to HLF-enabled Internet of Things (IoT) use cases. We propose a novel set of criteria for evaluating HLF latency performance modelling and highlight key HLF parameters that influence latency, aligning them with our evaluation criteria. We then classify existing papers based on their focus on latency modelling and the criteria they address. Additionally, we provide a comprehensive overview of latency performance modelling from various researchers, emphasizing the challenges in adapting these models to HLF-enabled IoT blockchain within the framework of our evaluation criteria. Finally, we suggest directions for future research and highlight open research questions for further exploration

Passive IoT Device-Type Identification Using Few-Shot Learning

The ever-growing number and diversity of connected devices have contributed to rising network security challenges. Vulnerable and unauthorized devices may pose a significant security risk with severe consequences. Device-type identification is instrumental in reducing risk and thwarting cyberattacks that may be caused by vulnerable devices. At present, IoT device identification methods use traditional machine learning or deep learning techniques, which require a large amount of labeled data to generate the device fingerprints. Moreover, these techniques require building a new model whenever a new device is introduced. To address these limitations, we propose a few-shot learning-based approach on siamese neural networks to identify IoT device-type connected to a network by analyzing their network communications, which can be effective under conditions of insufficient labeled data and/or resources. We evaluate our method on data obtained from real-world IoT devices. The experimental results show the effectiveness of the proposed method even with a small amount of data samples. Besides, it indicates that our approach outperforms IoT Sentinel, the state-of-the-art approach for IoT fingerprinting, by a margin of 10% additional accuracy

Artificial Intelligence Of Things For Ubiquitous Sports Analytics

To enable mobile devices to perform in-the-wild sports analytics, particularly swing tracking, remains an open question. A crucial challenge is to develop robust methods that can operate across various sports (e.g., golf and tennis), different sensors (cameras and IMU), and diverse human users. Traditional approaches typically rely on vision-based or IMU-based methods to extract key points from subjects in order to estimate trajectory predictions. However, these methods struggle to generate accurate swing tracking, as vision-based techniques are susceptible to occlusion, and IMU sensors are notorious for accumulated errors. In this thesis, we propose several innovative solutions by leveraging AIoT, including the IoT with ubiquitous wearable devices such as smartphones and smart wristbands, and harnessing the power of AI such as deep neural networks, to achieve ubiquitous sports analytics. We make three main technical contributions: a tailored deep neural network design, network model automatic search, and model domain adaptation to address the problem of heterogeneity among devices, human subjects, and sports for ubiquitous sports analytics. In Chapter 2, we begin with the design of a prototype that combines IMU and depth sensor fusion, along with a tailored deep neural network, to address the occlusion problems faced by depth sensors during swings. To recover swing trajectories with fine-grained details, we propose a CNN-LSTM architecture that learns multi-modalities within depth and IMU sensor fusion. In Chapter 3, we develop a framework to reduce the overhead of model design for new devices, sports, and human users. By designing a regression-based stochastic NAS method, we improve swing-tracking algorithms through automatic model generation. We also extend our studies to include unseen human users, sensor devices, and sports. Leveraging a domain adaptation method, we propose a framework that eliminates the need for tedious training data collection and labeling for new users, devices, and sports via adversarial learning. In Chapter 4, we present a framework to alleviate the model parameter selection process in NAS, as introduced in Chapter 3. By employing zero-cost proxies, we search for the optimal swing tracking architecture without training, in a significantly larger candidate model pool. We demonstrate that the proposed method outperforms state-of-the-art approaches in swing tracking, as well as in adapting to different subjects, sports, and devices. Overall, this thesis develops a series of innovative machine learning algorithms to enable ubiquitous IoT wearable devices to perform accurate swing analytics (e.g., tracking, analysis, and assessment) in real-world conditions