Biblio

The increasing volume of domestic and foreign trade brings new challenges to the efficiency and safety supervision of transportation. With the rapid development of Internet technology, it has opened up a new era of intelligent Internet of Things and the modern marine Internet of Vessels. Radio Frequency Identification technology strengthens the intelligent navigation and management of ships through the unique identification function of “label is object, object is label”. Intelligent Internet of Vessels can achieve the function of “limited electronic monitoring and unlimited electronic deterrence” combined with marine big data and Cyber Physical Systems, and further improve the level of modern maritime supervision and service.

The popular federated edge learning (FEEL) framework allows privacy-preserving collaborative model training via frequent learning-updates exchange between edge devices and server. Due to the constrained bandwidth, only a subset of devices can upload their updates at each communication round. This has led to an active research area in FEEL studying the optimal device scheduling policy for minimizing communication time. However, owing to the difficulty in quantifying the exact communication time, prior work in this area can only tackle the problem partially by considering either the communication rounds or per-round latency, while the total communication time is determined by both metrics. To close this gap, we make the first attempt in this paper to formulate and solve the communication time minimization problem. We first derive a tight bound to approximate the communication time through cross-disciplinary effort involving both learning theory for convergence analysis and communication theory for per-round latency analysis. Building on the analytical result, an optimized probabilistic scheduling policy is derived in closed-form by solving the approximate communication time minimization problem. It is found that the optimized policy gradually turns its priority from suppressing the remaining communication rounds to reducing per-round latency as the training process evolves. The effectiveness of the proposed scheme is demonstrated via a use case on collaborative 3D objective detection in autonomous driving.

The goal of neural style transfer is to transform images by the deep learning method, such as changing oil paintings into sketch-style images. The Generative Adversarial Network (GAN) has made remarkable achievements in neural style transfer in recent years. At first, this paper introduces three typical neural style transfer methods, including StyleGAN, StarGAN, and Transparent Latent GAN (TL-GAN). Then, we discuss the advantages and disadvantages of these models, including the quality of the feature axis, the scale, and the model's interpretability. In addition, as the core of this paper, we put forward innovative improvements to the above models, including how to fully exploit the advantages of the above three models to derive a better style conversion model.

The transmission and storage process for the power data in an intelligent grid has problems such as a single point of failure in the central node, low data credibility, and malicious manipulation or data theft. The characteristics of decentralization and tamper-proofing of blockchain and its distributed storage architecture can effectively solve malicious manipulation and the single point of failure. However, there are few safe and reliable data transmission methods for the significant number and various identities of users and the complex node types in the power blockchain. Thus, this paper proposes a collaborative control scheme between on-chain and off-chain power data based on the distributed oracle technology. By building a trusted on-chain transmission mechanism based on distributed oracles, the scheme solves the credibility problem of massive data transmission and interactive power data between smart contracts and off-chain physical devices safely and effectively. Analysis and discussion show that the proposed scheme can realize the collaborative control between on-chain and off-chain data efficiently, safely, and reliably.

The Z machine is a pulsed power generator located at Sandia National Laboratories in Albuquerque, New Mexico. It is capable of producing a \textbackslashtextgreater20 MA current pulse that is directed onto an experimental load. While a diverse array of experiments are conducted on the Z machine, including x-ray production and dynamic materials science experiments, the focus of this presentation are the Magnetic Liner Inertial Fusion (MagLIF) experiments. In these experiments, an axial magnetic field is applied to the load region, where a cylindrical, fuel-filled metal liner is imploded. We explore the effects of this field on the ability to efficiently couple the generator current to the load, and the extent to which this field interrupts the magnetic insulation of the inner-most transmission line. We find that at the present-day applied field values, the effects of the applied field on current coupling are negligible. Estimates of the potential impact on current coupling of the larger applied field values planned for future experiments are also given. Shunted current is measured with B-dot probes and flyer velocimetry techniques. Analytical calculations, 2D particle-in-cell simulations, and experimental measurements will be presented.

Human-automation shared control is proposed to reduce the risk of driver disengagement in Level-3 autonomous vehicles. Although previous studies have approved shared control strategy is effective to keep a driver in the loop and improve the driver's performance, over- and under-trust may affect the cooperation between the driver and the automation system. This study conducted a human-in-the-loop driving simulation experiment to assess the effects of trust on driver's behavior of shared control. An expert shared control strategy with longitudinal and lateral driving assistance was proposed and implemented in the experiment platform. Based on the experiment (N=24), trust in shared control was evaluated, followed by a correlation analysis of trust and behaviors. Moderating effects of trust on the relationship between gaze focalization and minimum of time to collision were then explored. Results showed that self-reported trust in shared control could be evaluated by three subscales respectively: safety, efficiency and ease of control, which all show stronger correlations with gaze focalization than other behaviors. Besides, with more trust in ease of control, there is a gentle decrease in the human-machine conflicts of mean brake inputs. The moderating effects show trust could enhance the decrease of minimum of time to collision as eyes-off-road time increases. These results indicate over-trust in automation will lead to unsafe behaviors, particularly monitoring behavior. This study contributes to revealing the link between trust and behavior in the context of human-automation shared control. It can be applied in improving the design of shared control and reducing risky behaviors of drivers by further trust calibration.

Return address authentication mechanisms protect return addresses by calculating and checking their message authentication codes (MACs) at runtime. However, these works only provide empirical analysis on their security, and it is still unclear whether the attacker can bypass these defenses by launching reuse attacks.In this paper, we present a solution to quantitatively analysis the security of return address authentication mechanisms against reuse attacks. Our solution utilizes some libc functions that could leakage data from memory. First, we perform reaching definition analysis to identify the source of parameters of these functions. Then we infer how many MACs could be observed at runtime by modifying these parameters. Afterward, we select the gadgets that could be exploited by reusing these observed MACs. Finally, we stitch desired gadget to craft attacks. We evaluated our solution on 5 real-word applications and successfully crafted reuse attacks on 3 of them. We find that the larger an application is, the more libc functions and gadgets can be found and reused, and furthermore, the more likely the attack is successfully crafted.

How to obtain accurate travel time predictions is among the most critical problems in Intelligent Transportation Systems (ITS). Recent literature has shown the effectiveness of machine learning models on travel time forecasting problems. However, most of these models predict travel time in a point estimation manner, which is not suitable for real scenarios. Instead of a determined value, the travel time within a future time period is a distribution. Besides, they all use grid structure data to obtain the spatial dependency, which does not reflect the traffic network's actual topology. Hence, we propose GCGTTE to estimate the travel time in a distribution form with Graph Deep Learning and Generative Adversarial Network (GAN). We convert the data into a graph structure and use a Graph Neural Network (GNN) to build its spatial dependency. Furthermore, GCGTTE adopts GAN to approximate the real travel time distribution. We test the effectiveness of GCGTTE with other models on a real-world dataset. Thanks to the fine-grained spatial dependency modeling, GCGTTE outperforms the models that build models on a grid structure data significantly. Besides, we also compared the distribution approximation performance with DeepGTT, a Variational Inference-based model which had the state-of-the-art performance on travel time estimation. The result shows that GCGTTE outperforms DeepGTT on metrics and the distribution generated by GCGTTE is much closer to the original distribution.

Surveillance videos can capture anomalies in real scenarios and play an important role in security systems. Anomaly events are unpredictable, which reflect the unsupervised nature of the problem. In addition, it is difficult to construct a complete video dataset which contains all normal events. Based on the diversity of normal events, this paper proposes a memory-enhanced unsupervised method for anomaly detection. The proposed method reconstructs video events by combining prototype features and encoded features to detect anomaly events. Furthermore, a memory module is introduced to better store the prototype patterns of normal events. Experimental results in various benchmark datasets demonstrate the effectiveness and robustness of the proposed method.

The theoretical basis made it possible to implement software for automated secure biometric verification and personal identification, which can be used by information security systems (including access control and management systems). The work is devoted to solving an urgent problem - the development of methods and algorithms for generating a key for a storage device based on biometric parameters. Biometric cryptosystems take advantage of biometrics to improve the security of encryption keys. The ability not to store a key that is derived from biometric data is a direct advantage of the method of generating cryptographic keys from biometric data of users over other existing encryption methods.

Internet of Things (IoT) connects various nodes such as sensor devices. For users from foreign networks, their direct access to the data of sensor devices is restricted because of security threats. Therefore, a ticket-based authentication scheme was proposed, which can mutually authenticate a mobile device and a sensor device. This scheme with new features fills a gap in IoT authentication, but the scheme has not been verified formally. Hence, it is important to study the security and reliability of the scheme from the perspective of formal methods.In this paper, we model this scheme using Communicating Sequential Processes (CSP). Considering the possibility of key leakage caused by security threats in IoT networks, we also build models where one of the keys used in the scheme is leaked. With the model checker Process Analysis Toolkit (PAT), we verify four properties (deadlock freedom, data availability, data security, and data authenticity) and find that the scheme cannot satisfy the last two properties with key leakage. Thus, we propose two improved models. The verification results show that the first improved model can guarantee data security, and the second one can ensure both data security and data authenticity.

Because network nodes are transparent in media streaming applications, traditional networks cannot utilize the scalability feature of Scalable video coding (SVC). Compared with the traditional network, SDN supports various flows in a more fine-grained and scalable manner via the OpenFlow protocol, making QoS requirements easier and more feasible. In previous studies, a Ternary Content-Addressable Memory (TCAM) space in the switch has not been considered. This paper proposes a scalable QoS-aware multicast scheme for SVC streams, and formulates the scalable QoS-aware multicast routing problem as a nonlinear programming model. Then, we design heuristic algorithms that reduce the TCAM space consumption and construct the multicast tree for SVC layers according to video streaming requests. To alleviate video quality degradation, a dynamic layered multicast routing algorithm is proposed. Our experimental results demonstrate the performance of this method in terms of the packet loss ratio, scalability, the average satisfaction, and system utility.

The focus of this work is the transient modelling of the DC-arc voltage on a Hybrid Switch (a mechanical switch in parallel with a static switch) of a key protection component called Fast Discharge Unit (FDU) in the Divertor Tokamak Test (DTT). The DTT facility is an experimental tokamak in advanced design and realization phase, which will be built in the ENEA Research Centre in Frascati (Italy). The FDU allows the safe discharge of the Toroidal Field (TF) superconducting magnets when a quench is detected or a failure occurs in the power supply or in the cryogenic system. In this work, the arc conductance of the mechanical By-Pass Switch (BPS) of the Hybrid Switch is modelled using the well-known Mayr-Cassie equations and the Paukert arc parameters. The simulations show a good agreement with the expected results in terms of voltage and current transient from the mechanical switch to the static switch.

With the rapid development of Internet of Things technology and sensor networks, large amount of data is facing security challenges in the transmission process. In the process of data transmission, the standardization and authentication of data sources are very important. A digital signature scheme based on bilinear pairing problem is designed. In this scheme, by signing the authorization mechanism, the management node can control the signature process and distribute data. The use of private key segmentation mechanism can reduce the performance requirements of sensor nodes. The reasonable combination of timestamp mechanism can ensure the time limit of signature and be verified after the data is sent. It is hoped that the implementation of this scheme can improve the security of data transmission on the Internet of things environment.

With the development of network, network security has become a topic of increasing concern. Recent years, machine learning technology has become an effective means of network intrusion detection. However, machine learning technology requires a large amount of data for training, and training data often contains privacy information, which brings a great risk of privacy leakage. At present, there are few researches on data privacy protection in the field of intrusion detection. Regarding the issue of privacy and security, we combine differential privacy and machine learning algorithms, including One-class Support Vector Machine (OCSVM) and Local Outlier Factor(LOF), to propose an hybrid intrusion detection system (IDS) with privacy protection. We add Laplacian noise to the original network intrusion detection data set to get differential privacy data sets with different privacy budgets, and proposed a hybrid IDS model based on machine learning to verify their utility. Experiments show that while protecting data privacy, the hybrid IDS can achieve detection accuracy comparable to traditional machine learning algorithms.

Pedestrian dead reckoning (PDR) is a widely used approach to estimate locations and trajectories. Accessing location-based services with trajectory data can bring convenience to people, but may also raise privacy concerns that need to be addressed. In this paper, a privacy-preserving pedestrian dead reckoning framework is proposed to protect a user’s trajectory privacy based on differential privacy. We introduce two metrics to quantify trajectory privacy and data utility. Our proposed privacy-preserving trajectory extraction algorithm consists of three mechanisms for the initial locations, stride lengths and directions. In addition, we design an adversary model based on particle filtering to evaluate the performance and demonstrate the effectiveness of our proposed framework with our collected sensor reading dataset.

This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.

This work investigates the vulnerability of Gaussian Mixture Model (GMM) i-vector based speaker verification systems to adversarial attacks, and the transferability of adversarial samples crafted from GMM i-vector based systems to x-vector based systems. In detail, we formulate the GMM i-vector system as a scoring function of enrollment and testing utterance pairs. Then we leverage the fast gradient sign method (FGSM) to optimize testing utterances for adversarial samples generation. These adversarial samples are used to attack both GMM i-vector and x-vector systems. We measure the system vulnerability by the degradation of equal error rate and false acceptance rate. Experiment results show that GMM i-vector systems are seriously vulnerable to adversarial attacks, and the crafted adversarial samples are proved to be transferable and pose threats to neural network speaker embedding based systems (e.g. x-vector systems).

The progress made in terms of controller technologies with the introduction of remotely-accessibility capacity in the digital controllers has opened the door to new cybersecurity threats on the Industrial Control Systems (ICSs). Among them, some aim at damaging the ICS's physical system. In this paper, a corrupted controller emitting a non-legitimate Pulse Width Modulation control signal to an Electro-Mechanical Actuator (EMA) is considered. The attacker's capabilities for accelerating the EMA's aging by inducing Partial Discharges (PDs) are investigated. A simplified model is considered for highlighting the influence of the carrier frequency of the control signal over the amplitude and the repetition of the PDs involved in the EMA's aging.

In this modern, technological age, the internet has been adopted by the masses. And with it, the danger of malicious attacks by cybercriminals have increased. These attacks are done via Malware, and have resulted in billions of dollars of financial damage. This makes the prevention of malicious attacks an essential part of the battle against cybercrime. In this paper, we are applying machine learning algorithms to predict the malware infection rates of computers based on its features. We are using supervised machine learning algorithms and gradient boosting algorithms. We have collected a publicly available dataset, which was divided into two parts, one being the training set, and the other will be the testing set. After conducting four different experiments using the aforementioned algorithms, it has been discovered that LightGBM is the best model with an AUC Score of 0.73926.

This paper presents how students practical’s is developed and used for the important task forensic specialist have to do when using common digital forensic tools for data deletion and data recovery from various types of digital media and live systems. Digital forensic tools like EnCase, FTK imager, BlackLight, and open source tools are discussed in developed practical’s scenarios. This paper shows how these tools can be used to train and enhance student understanding of the capabilities and limitations of digital forensic tools in uncommon digital forensic scenarios. Students’ practicals encourage students to efficiently use digital forensic tools in the various professional scenarios that they will encounter.

The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.

This paper describes research results of the possibility of developing a methodology to implement systematic knowledge about adversaries` tactics and techniques into the process of determining requirements for information security system and evaluating defensive countermeasures.

Particle swarm optimization (PSO), as a kind of swarm intelligence algorithm, has the advantages of simple algorithm principle, less programmable parameters and easy programming. Many scholars have applied particle swarm optimization (PSO) to various fields through learning it, and successfully solved linear problems, nonlinear problems, multiobjective optimization and other problems. However, the algorithm also has obvious problems in solving problems, such as slow convergence speed, too early maturity, falling into local optimization in advance, etc., which makes the convergence speed slow, search the optimal value accuracy is not high, and the optimization effect is not ideal. Therefore, many scholars have improved the particle swarm optimization algorithm. Taking into account the improvement ideas proposed by scholars in the early stage and the shortcomings still existing in the improvement, this paper puts forward the idea of improving particle swarm optimization algorithm in the future.

While backtracking analysis has been successful in assisting the investigation of complex security attacks, it faces a critical dependency explosion problem. To address this problem, security analysts currently need to tune backtracking analysis manually with different case-specific heuristics. However, existing systems fail to fulfill two important system requirements to achieve effective backtracking analysis. First, there need flexible abstractions to express various types of heuristics. Second, the system needs to be responsive in providing updates so that the progress of backtracking analysis can be frequently inspected, which typically involves multiple rounds of manual tuning. In this paper, we propose a novel system, APTrace, to meet both of the above requirements. As we demonstrate in the evaluation, security analysts can effectively express heuristics to reduce more than 99.5% of irrelevant events in the backtracking analysis of real-world attack cases. To improve the responsiveness of backtracking analysis, we present a novel execution-window partitioning algorithm that significantly reduces the waiting time between two consecutive updates (especially, 57 times reduction for the top 1% waiting time).