In the current literature, many solutions for solving blockchain scaling have been tried historically, whereas most of them usually may compromise the decentralisation. Ethereum has chosen to scale by switching to Proof of Stake consensus and adding data sharding to allow Layer 2 execution to be cheaper. However, in the light of cross-domain Maximal Extractable Value (MEV), even this strategy may have centralising forces built-in. In this work, we focus on cross-domain MEV and try to identify cross domain arbitrage. In particular, we extract Uniswap data from four different domains and provide an initial analysis of how to identify cross domain arbitrages.

With the rapid development of information technology, the ethical use of data and users' privacy has become a big concern. In European Union, the General Data Protection Regulation (GDPR) has been enforced since 2018, aiming to protect a person's privacy. However, the growth of the data economy might be hindered due to the lack of a trusted, secure and privacy-aware tool. Motivated by this observation, this work presents Data Vaults - a secure, distributed and privacy-preserving personal data management platform. The main goal is to mitigate various privacy concerns through allowing an individual to maintain the ownership, handle and share the data based on their willingness. The platform enables a flexible data sharing method with fair compensation schemes. In particular, with the Data Vaults platform, an individual can protect the sharing of personal data and fairly define how value can be captured, created, released and cashed out for the benefit of all the stakeholders involved (companies or not).

In this work, we develop BlockPAT, a blockchain-enabled management system for the tokenization of second-hand physical assets, e.g., laptops. With this system, the information gap between buyers and sellers in the second-hand market will be eliminated, and with the help of a price oracle, the liquidity of the second-hand market can be greatly improved. Furthermore, our system is built upon the latest ZK-rollups solution; thus, the overall transaction cost and time delay will be limited to an affordable value.

In recent years, Graphical Processing Unit (GPU) is not only becoming a piece of hardware that accelerates graphics but also playing a key role in accelerating the fields of machine learning and artificial intelligence. The GPU’s heightened importance has led to increasing concern about the confidentiality of a GPU’s computing data as well as its internal communications. Although the GPU Trusted Execution Environment (TEE) has been implemented as a solution toward this issue, side-channel attacks in GPUs still remain as an open problem. In this work, we introduce Delay-masquerading Technique Upheld StrongBox (DTUBox) to strengthen the resilience of existing GPU TEE over StrongBox against side-channel attacks by injecting obfuscated noise with our developed algorithm, making the correlations difficult to reference between a task and workload. In our evaluation, we demonstrate that with only around 5% performance overhead, our approach could effectively lower the correlation rate to 38% between the original behavior sequences and the obfuscated sequences.

The advancement of information technology allows the creation of smart devices that not only are programable, but also can perform machine-to-machine communication in order to reach a flexible large-scale manufacturing strategy in Industry 4.0. However, as more components are connected to the Internet, cyber-criminals can perform malicious actions remotely. As one lasting threat, sabotaging smart devices' execution integrity can cause a large financial loss, i.e., causing malfunctioning. Hence, it is important to secure the execution integrity of smart devices in Industry 4.0. Motivated by the emerging blockchain technology, in this paper, we focus on how blockchain can help Industry 4.0 application protect execution integrity and propose a blockchain-based execution protection scheme namedNoSneaky, which is low-cost and can be easily integrated into the current production systems. In the evaluation, we demonstrate its performance and effectiveness in securing the execution integrity.

Electroencephalography (EEG) is the record of electrogram of the electrical activity on the scalp typically using non-invasive electrodes. In recent years, many studies started using EEG as a human characteristic to construct biometric identification or authentication. Being a kind of behavioral characteristics, EEG has its natural advantages whereas some characteristics have not been fully evaluated. For instance, we find that Motor Imagery (MI) brain-computer interface is mainly used for improving neurological motor function, but has not been widely studied in EEG authentication. Currently, there are many mature methods for understanding such signals. In this paper, we propose an enhanced EEG authentication framework with Motor Imagery, by offering a complete EEG signal processing and identity verification. Our framework integrates signal preprocess, channel selection and deep learning classification to provide an end-to-end authentication. In the evaluation, we explore the requirements of a biometric system such as uniqueness, permanency, collectability, and investigate the framework regarding insider and outsider attack performance, cross-session performance, and influence of channel selection. We also provide a large comparison with state-of-the-art methods, and our experimental results indicate that our framework can provide better performance based on two public datasets.

With the development of Internet of Things (IoT), network security has become very important as cyber-attackers can easily compromise such distributed networks and systems. An intrusion detection system (IDS) is a basic and essential security mechanism to detect malicious traffic. In the literature, in addition to traditional machine learning algorithms, many deep learning schemes have been examined to enhance the detection performance. However, insufficient amounts of labeled samples are still a challenge for real-world implementation, especially in some scenarios such as smart home and Internet of Vehicles. To address this issue, we explore transfer learning as a promising solution. In this work, we develop TDL-IDS, a transfer deep learning based IDS that can work with limited labeled data items. Our approach first uses Long Short Term Memory (LSTM) to train a model on the source domain and then leverages transfer learning to continue the training process on the target domain. In the evaluation, we use NSL-KDD as the source domain, and AWID as the target domain. Our results indicate that TDL-IDS can outperform many similar approaches.

Civil aviation (aircraft) is one of the public transportation industries that requires the highest safety standards. However, several accidents on aircraft maintenance system had revealed that there is a need to secure the record integrity and traceability. Motivated by this issue and supported by an airline, in this work, we design and implement AirChain, a blockchain-based aircraft maintenance record system, in which the data can be stored in a way that is both resistant to tampering and easy to access. The experimental results indicate the viability and practicability of our system.

The importance of efficient and accessible parking systems has been growing over the past decades. Steady growth in urban population and car ownership has increased problems with traffic congestion and air pollution caused by inadequate parking systems. Blockchain-based parking systems have been proposed to increase system availability and resilience and improve trust among participants. However, these systems are not transferable to the parking systems of European cities such as Copenhagen, as they are based on assumptions about the parking infrastructure, which do not hold, and are inherently incompatible with regional privacy protection regulations such as the GDPR. Furthermore, many blockchain solutions suffer from scalability issues, severely limiting their efficiency. In this work, we develop a blockchainbased parking system in Denmark, aiming to make up the gap in the existing research by directly considering GDPR compliance. Our work focuses on the municipal parking system for on-street parking in Copenhagen (Denmark), where Hyperledger Fabric is used to maintain a trusted distributed ledger for parking data shared by the network. Personal data is protected through offchain storage while maintaining on-chain verifiability. The proposed system is implemented as a proof-of-concept application, which can deliver sufficient throughput to support the needs of municipal parking.

The Internet of Things (IoT) implements a tremendous environment of extensive data streams, whereby any suspicious activities should be detected to safeguard systems' reliability and availability. Distributed Denial of Service (DDoS) attack is a major threat on computer networks, in which an attacker can send huge traffic with multiple IP addresses or machines. In this work, we focus on detecting DDoS attacks, and design Network Gate Shield (NGS), a tool that works on SDN architecture based on the RYU controller. It can examine the traffic trustworthiness, and then determine whether the current traffic is normal based on packet specification, such as the average packet size and the packet per-sec threshold. In the evaluation with an emulated environment, our experimental results indicate that NGS is viable and effective in mitigating DDoS traffic compared with several similar detection approaches.

In modern times, the definition and the library’s expected functionality did not change much as before. It is still a place for us to hold massive collections of information. Traditionally, libraries require physical storage space for writings and publications, but storing and managing costs can be tremendous. Although the aid of digital promises and computers allows a super high density of information storage, it does not lower the library’s complexity. As our main source of information is moving away from physical writings toward digital, the new digital library (i.e., state-run library) faces the challenges of records’ integrity and storage efficiency. Focused on this issue, we learn the demands from the Royal Library in Denmark and try to explore the use of blockchain technology. We introduce a system named LibBlock, by integrating with both smart contract and IPFS in order to provide a robust, decentralized, flexible, and adaptive e-Library, which enables the ease of scalability and rigid record keeping. In the evaluation, we investigate the initial performance of LibBlock with Ethereum and show its viability and efficiency.

Driven by an increasing number of connected medical devices, Internet of Medical Things (IoMT), as an application of Internet of Things (IoT) in healthcare, is developed to help collect, analyze, and transmit medical data. During the outbreak of a pandemic like COVID-19, IoMT can be useful to monitor the status of patients and detect main symptoms remotely, by using various smart sensors. However, due to the lack of emotional care in the current IoMT, it is still a challenge to reach an efficient medical process. Especially under COVID-19, there is a need to monitor emotional status among particular people like the elderly. In this work, we propose an emotion-aware healthcare monitoring system in IoMT, based on brainwaves. With the fast development of electroencephalography (EEG) sensors in current headsets and some devices, brainwave-based emotion detection becomes feasible. The IoMT devices are used to capture the brainwaves of a patient in a scenario of smart home. Also, our system involves the analysis of touch behavior as the second layer to enhance the brainwave-based emotion recognition. In the user study with 60 participants, the results indicate the viability and effectiveness of our approach in detecting emotions like comfortable and uncomfortable, which can complement existing emotion-aware healthcare applications and mechanisms.