Biblio

The (IoT) paradigm’s fundamental goal is to massively connect the “smart things” through standardized interfaces, providing a variety of smart services. Cyber-Physical Systems (CPS) include both physical and cyber components and can apply to various application domains (smart grid, smart transportation, smart manufacturing, etc.). The Digital Twin (DT) is a cyber clone of physical objects (things), which will be an essential component in CPS. This paper designs a systematic taxonomy to explore different attacks on DT-based CPS and how they affect the system from a four-layer architecture perspective. We present an attack space for DT-based CPS on four layers (i.e., object layer, communication layer, DT layer, and application layer), three attack objects (i.e., confidentiality, integrity, and availability), and attack types combined with strength and knowledge. Furthermore, some selected case studies are conducted to examine attacks on representative DT-based CPS (smart grid, smart transportation, and smart manufacturing). Finally, we propose a defense mechanism called Secured DT Development Life Cycle (SDTDLC) and point out the importance of leveraging other enabling techniques (intrusion detection, blockchain, modeling, simulation, and emulation) to secure DT-based CPS.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is an important security technique designed to deter bots from abusing software systems, which has broader applications in cyberspace. CAPTCHAs come in a variety of forms, including the deciphering of obfuscated text, transcribing of audio messages, and tracking mouse movement, among others. This paper focuses on using deep learning techniques to recognize text-based CAPTCHAs. In particular, our work focuses on generating training datasets using different CAPTCHA schemes, along with a pre-processing technique allowing for character-based recognition. We have encapsulated the CRABI (CAPTCHA Recognition with Attached Binary Images) framework to give an image multiple labels for improvement in feature extraction. Using real-world datasets, performance evaluations are conducted to validate the efficacy of our proposed approach on several neural network architectures (e.g., custom CNN architecture, VGG16, ResNet50, and MobileNet). The experimental results confirm that over 90% accuracy can be achieved on most models.

To improve efficiency and resource usage in data retrieval, an Internet of Things (IoT) search engine organizes a vast amount of scattered data and responds to client queries with processed results. Machine learning provides a deep understanding of complex patterns and enables enhanced feedback to users through well-trained models. Nonetheless, machine learning models are prone to adversarial attacks via the injection of elaborate perturbations, resulting in subverted outputs. Particularly, adversarial attacks on time-series data demand urgent attention, as sensors in IoT systems are collecting an increasing volume of sequential data. This paper investigates adversarial attacks on time-series analysis in an IoT search engine (IoTSE) system. Specifically, we consider the Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) as our base model, implemented in a simulated federated learning scheme. We propose the Federated Adversarial Learning for IoT Search Engine (FALIoTSE) that exploits the shared parameters of the federated model as the target for adversarial example generation and resiliency. Using a real-world smart parking garage dataset, the impact of an attack on FALIoTSE is demonstrated under various levels of perturbation. The experiments show that the training error increases significantly with noises from the gradient.

As it was difficult for the State Grid Corporation of China (SGCC) to manage a large amount of safety equipment efficiently, resulting in the frequent occurrence of safety accidents caused by the quality of equipment. Therefore, this paper presents a design of a self-powered wireless communication radio frequency identification tag system based on the Si24R1. The system uses blockchain technology to provide a full-length, chain-like path for RFID big data to achieve data security management. Using low-power Si24R1 chips to make tags can extend the use time of tags and achieve full life cycle management of equipment. In addition, a transmission scheme was designed to reduce the packet loss rate, in this paper. Finally, the result showed that the device terminal received and processed information from the six tags simultaneously. According to calculations, this electronic tag could be used for up to three years. This system can be widely used for safe operation management, which can effectively reduce the investment of manpower and material resources.

In order to solve the statistical fluctuation problem caused by the finite data length in the practical quantum key distribution system, four commonly used statistical methods, DeMoivre-Laplace theorem, Chebyshev inequality, Chernoff boundary and Hoeffding boundary, are used to analyze. The application conditions of each method are discussed, and the effects of data length and confidence level on quantum key distribution security performance are simulated and analyzed. The simulation results show that the applicable conditions of Chernoff boundary are most consistent with the reality of the practical quantum key distribution system with finite-length data. Under the same experimental conditions, the secure key generation rate and secure transmission distance obtained by Chernoff boundary are better than those of the other three methods. When the data length and confidence level change, the stability of the security performance obtained by the Chernoff boundary is the best.

The orthogonal matching pursuit (OMP) algorithm is a commonly used algorithm for recovering K-sparse signals x ∈ ℝn from linear model y = Ax, where A ∈ ℝm×n is a sensing matrix. A fundamental question in the performance analysis of OMP is the characterization of the probability that it can exactly recover x for random matrix A. Although in many practical applications, in addition to the sparsity, x usually also has some additional property (for example, the nonzero entries of x independently and identically follow the Gaussian distribution), none of existing analysis uses these properties to answer the above question. In this paper, we first show that the prior distribution information of x can be used to provide an upper bound on \textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar21/\textbackslashtextbar\textbackslashtextbarx\textbackslashtextbar\textbackslashtextbar22, and then explore the bound to develop a better lower bound on the probability of exact recovery with OMP in K iterations. Simulation tests are presented to illustrate the superiority of the new bound.

Software Defined Radio (SDR) can move the complicated signal processing and handling procedures involved in communications from radio equipment into computer software. Consequently, SDR equipment could consist of only a few chips connected to an antenna. In this paper, we present an implemented SDR testbed, which consists of four complete SDR nodes. Using the designed testbed, we have conducted two case studies. The first is designed to facilitate video transmission via adaptive LTE links. Our experimental results demonstrate that adaptive LTE link video transmission could reduce the bandwidth usage for data transmission. In the second case study, we perform UE location estimation by leveraging the signal strength from nearby cell towers, pertinent to various applications, such as public safety and disaster rescue scenarios where GPS (Global Position System) is not available (e.g., indoor environment). Our experimental results show that it is feasible to accurately derive the location of a UE (User Equipment) by signal strength. In addition, we design a Hardware In the Loop (HIL) simulation environment using the Vienna LTE simulator, srsLTE library, and our SDR testbed. We develop a software wrapper to connect the Vienna LTE simulator to our SDR testbed via the srsLTE library. Our experimental results demonstrate the comparative performance of simulated UEs and eNodeBs against real SDR UEs and eNodeBs, as well as how a simulated environment can interact with a real-world implementation.