Biblio

In signed directed graph (SDG) fault diagnosis model, only single fault can be diagnosed. In order to meet the requirements of multiple faults diagnosis, in this paper, improved signed directed graph (ISDG) fault diagnosis model was proposed. The logic and influence between nodes were included in ISDG model. With ISDG model, complex logic can be shown, multiple faults can be diagnosed and the optimal sequence can be determined. Two algorithms are proposed in this paper. One algorithm can obtain the multiple faults combine logic, and the other algorithm can obtain the optimal path of fault diagnosis. According to these two algorithms, the efficiency was improved and the cost was reduced in the multiple fault diagnosis process. Finally, the faults of an aircraft engine bleed system were diagnosed with the interactive algorithm. The proposed algorithms can obtain a diagnosis result effectively. The results of two cases prove that these algorithms can be used for multiple fault diagnosis.

Dynamometer cards can reflect different down-hole working conditions of sucker rod pumping wells. It has great significances to realize multiple fault diagnosis for actual oilfield production. In this paper, the extension theory is used to build a matter-element model to describe the fault diagnosis problem of the sucker rod pumping wells. The correlation function is used to calculate the correlation degree between the diagnostic fault and many standard fault types. The diagnosed sample and many possible fault types are divided into different combinations according to the correlation degree; the F-statistics of each combination is calculated and the “unbiased transformation” is used to find the mean of interval vectors. Larger F-statistics means greater differences within the faults classification; and the minimum F-statistics reflects the real multiple fault types. Case study shows the effectiveness of the proposed method.

Hydraulic systems is a class of nonlinear complex systems. There are many typical characteristics with the systems: multiple functional components, multiple operation modes, space-time coupling work, and monitoring signals for faults are multivariate time series data, etc. Because of the characteristics, fault diagnosis for Hydraulic systems is not easy. Traditional fault diagnosis methods mostly ignore the multivariable timing characteristics of monitoring signals, it has made many detection and diagnosis (especially for multiple fault) can not keep high accuracy, and some of the methods are not even be able to multiple fault diagnosis. Aim at the problem, a multivariate time series classification based diagnosis method is proposed. Firstly, extracting timing characteristics (transformed features) from the time series data collected via sensors by 1-NN method. Secondly, training the transformed features by multi-class OVO-SVM to classify multivariate time series. Simulation of the method contains single fault and multiple faults conditions, the results show that the method has high accuracy, it can complete multiple-faults classification.

In fault diagnosis of industrial process, there are usually more than one variable that are faulty. When multiple faults occur, the generalized reconstruction-based contribution can be helpful while traditional RBC may make mistakes. Due to the correlation between the variables, these faults usually propagate to other normal variables, which is called smearing effect. Thus, it is helpful to consider the pervious fault diagnosis results. In this paper, a data-driven fault diagnosis method which is based on generalized RBC and bayesian decision is presented. This method combines multi-dimensional RBC and bayesian decision. The proposed method improves the diagnosis capability of multiple and minor faults with greater noise. A numerical simulation example is given to show the effectiveness and superiority of the proposed method.

This paper addresses analysis and design of multiple fault diagnosis for a class of Lipschitz nonlinear system. In order to automatically estimate multi-fault parameters efficiently, a new method of multi-fault diagnosis based on the differential evolution algorithm (DE) is proposed. Finally, a series of experiments validate the feasibility and effectiveness of the proposed method. The simulation show the high accuracy of the proposed strategies in multiple abrupt faults diagnosis.

The choice of threshold is an important part of fault diagnosis. Most of the current methods use a constant threshold for detection and it is difficult to meet the robustness and sensitivity requirements of the diagnosis system. This article develops a dynamic threshold algorithm for aircraft engine fault detection and isolation systems. The algorithm firstly analyzes the bounded norm uncertainty that may appear in the process of model based on the state space equation, and gives the time domain response range calculation formula under the influence of uncertain parameters; then the Kalman filter is combined to calculate the threshold with the real-time change of state; the simulation is performed at the end. The simulation results show that dynamic threshold range changes with status in real time.

Electric powertrains are widely used in automotive and renewable energy industries. Reliable diagnosis for defects in the critical components such as bearings, gears and stator windings, is important to prevent failures and enhance the system reliability and power availability. Most of existing fault diagnosis methods are based on specific characteristic frequencies to single faults at constant speed operations. Once multiple faults occur in the system, such a method may not detect the faults effectively and may give false alarms. Furthermore, variable speed operations render a challenge of analysing nonstationary signals. In this work, a deep learning-based fault diagnosis method is proposed to detect common faults in the electric powertrains. The proposed method is based on pattern recognition using convolutional neural network to detect effectively not only single faults at constant speed but also multiple faults in variable speed operations. The effectiveness of the proposed method is validated via an in-house experimental setup.

Principal Component Analysis (PCA) is a basic method of fault diagnosis based on multivariate statistical analysis. It utilizes the linear correlation between multiple process variables to implement process fault diagnosis and has been widely used. Traditional PCA fault diagnosis ignores the impact of faults with different magnitudes on detection accuracy. Based on a variety of data processing methods, this paper proposes a multi-level and multi-granularity principal component analysis method to make the detection results more accurate.

Network intrusion detection is an important component of network security. Currently, the popular detection technology used the traditional machine learning algorithms to train the intrusion samples, so as to obtain the intrusion detection model. However, these algorithms have the disadvantage of low detection rate. Deep learning is more advanced technology that automatically extracts features from samples. In view of the fact that the accuracy of intrusion detection is not high in traditional machine learning technology, this paper proposes a network intrusion detection model based on convolutional neural network algorithm. The model can automatically extract the effective features of intrusion samples, so that the intrusion samples can be accurately classified. Experimental results on KDD99 datasets show that the proposed model can greatly improve the accuracy of intrusion detection.

To be prepared against cyberattacks, most organizations resort to security information and event management systems to monitor their infrastructures. These systems depend on the timeliness and relevance of the latest updates, patches and threats provided by cyberthreat intelligence feeds. Open source intelligence platforms, namely social media networks such as Twitter, are capable of aggregating a vast amount of cybersecurity-related sources. To process such information streams, we require scalable and efficient tools capable of identifying and summarizing relevant information for specified assets. This paper presents the processing pipeline of a novel tool that uses deep neural networks to process cybersecurity information received from Twitter. A convolutional neural network identifies tweets containing security-related information relevant to assets in an IT infrastructure. Then, a bidirectional long short-term memory network extracts named entities from these tweets to form a security alert or to fill an indicator of compromise. The proposed pipeline achieves an average 94% true positive rate and 91% true negative rate for the classification task and an average F1-score of 92% for the named entity recognition task, across three case study infrastructures.

Network attack is a significant security issue for modern society. From small mobile devices to large cloud platforms, almost all computing products, used in our daily life, are networked and potentially under the threat of network intrusion. With the fast-growing network users, network intrusions become more and more frequent, volatile and advanced. Being able to capture intrusions in time for such a large scale network is critical and very challenging. To this end, the machine learning (or AI) based network intrusion detection (NID), due to its intelligent capability, has drawn increasing attention in recent years. Compared to the traditional signature-based approaches, the AI-based solutions are more capable of detecting variants of advanced network attacks. However, the high detection rate achieved by the existing designs is usually accompanied by a high rate of false alarms, which may significantly discount the overall effectiveness of the intrusion detection system. In this paper, we consider the existence of spatial and temporal features in the network traffic data and propose a hierarchical CNN+RNN neural network, LuNet. In LuNet, the convolutional neural network (CNN) and the recurrent neural network (RNN) learn input traffic data in sync with a gradually increasing granularity such that both spatial and temporal features of the data can be effectively extracted. Our experiments on two network traffic datasets show that compared to the state-of-the-art network intrusion detection techniques, LuNet not only offers a high level of detection capability but also has a much low rate of false positive-alarm.

Conventional methods for anomaly detection include techniques based on clustering, proximity or classification. With the rapidly growing social networks, outliers or anomalies find ingenious ways to obscure themselves in the network and making the conventional techniques inefficient. In this paper, we utilize the ability of Deep Learning over topological characteristics of a social network to detect anomalies in email network and twitter network. We present a model, Graph Neural Network, which is applied on social connection graphs to detect anomalies. The combinations of various social network statistical measures are taken into account to study the graph structure and functioning of the anomalous nodes by employing deep neural networks on it. The hidden layer of the neural network plays an important role in finding the impact of statistical measure combination in anomaly detection.

Research in combating misinformation reports many negative results: facts may not change minds, especially if they come from sources that are not trusted. Individuals can disregard and justify lies told by trusted sources. This problem is made even worse by social recommendation algorithms which help amplify conspiracy theories and information confirming one's own biases due to companies' efforts to optimize for clicks and watch time over individuals' own values and public good. As a result, more nuanced voices and facts are drowned out by a continuous erosion of trust in better information sources. Most misinformation mitigation techniques assume that discrediting, filtering, or demoting low veracity information will help news consumers make better information decisions. However, these negative results indicate that some news consumers, particularly extreme or conspiracy news consumers will not be helped. We argue that, given this background, technology solutions to combating misinformation should not simply seek facts or discredit bad news sources, but instead use more subtle nudges towards better information consumption. Repeated exposure to such nudges can help promote trust in better information sources and also improve societal outcomes in the long run. In this article, we will talk about technological solutions that can help us in developing such an approach, and introduce one such model called Trust Nudging.

Today, Internet of Things (IoT) implements an ecosystem where a panoply of interconnected devices collect data from physical environments and supply them to processing services, on top of which cloud-based applications are built and provided to mobile end users. The undebatable advantages of smart IoT systems clash with the need of a secure and trustworthy environment. In this paper, we propose a service-based methodology based on blockchain and smart contracts for trustworthy evidence collection at the basis of a trustworthy IoT assurance evaluation. The methodology balances the provided level of trustworthiness and its performance, and is experimentally evaluated using Hyperledger fabric blockchain.

Many blockchain transactions require blockchain-external data sources to provide data. Oracle systems have been proposed as a link between blockchains and blockchain-external resources. However, these Oracle systems vary greatly in assumptions and applicability and each system addresses the challenge of data on-chaining partly. We argue that Data On-chaining must be done in a trustworthy manner and, as a first contribution, define a set of key requirements for Trustworthy Data On-chaining. Further, we provide an in-depth assessment and comparison of state-of-the-art Oracle systems with regards to these requirements. This differentiation pinpoints the need for a uniform understanding of and directions for future research on Trustworthy Data On-chaining.

Internet of Things (IoT) as per estimated will connect 50 billion devices by 2020. Since its evolution, IoT technology provides lots of flexibility to develop and implement any application. Most of the application improves the human living standard and also makes life easy to access and monitoring the things in real time. Though there exist some security and privacy issues in IoT system like authentication, computation, data modification, trust among users. In this paper, we have identified the IoT application like insurance, supply chain system, smart city and smart car where trust among associated users is an major issue. The current centralized system does not provide enough trust between users. Using Blockchain technology we have shown that trust issue among users can be managed in a decentralized way so that information can be traceable and identify/verify any time. Blockchain has properties like distributed, digitally share and immutable which enhance security. For Blockchain implementation, Ethereum platform is used.

TEE(Trusted Execution Environment) has became one of the most popular security features for mobile platforms. Current TEE solutions usually implement secure functions in Trusted applications (TA) running over a trusted OS in the secure world. Host App may access these secure functions through the TEE driver. Unfortunately, such architecture is not very secure. A trusted OS has to be loaded in secure world to support TA running. Thus, the code size in secure world became large. As more and more TA is installed, the secure code size will be further larger and larger. Lots of real attack case have been reported [1]. In this paper, we present a novel TEE constructing method named ALTEE. Different from existing TEE solutions, ALTEE includes secure code in host app, and constructs a trustworthy execution environment for it dynamically whenever the code needs to be run.

This paper investigates the problem of user pairing in multi-carrier non-orthogonal multiple access (MC-NOMA) systems. Firstly, the hard channel capacity and soft channel capacity are presented. The former depicts the transmission capability of the system that depends on the channel conditions, and the latter refers to the effective throughput of the system that is determined by the actual user demands. Then, two optimization problems to maximize the hard and soft channel capacities are established, respectively. Inspired by the multiagent deep reinforcement learning (MADRL) and convolutional neural network, the user paring network (UP-Net), based on the cooperative game and deep deterministic policy gradient, is designed for solving the optimization problems. Simulation results demonstrate that the performance of the designed UP-Net is comparable to that obtained from the exhaustive search method via the end-to-end low complexity method, which is superior to the common method, and corroborate that the UP-Net focuses more on the actual user demands to improve the soft channel capacity. Additionally and more importantly, the paper makes a useful exploration on the use of MADRL to solve the resource allocation problems in communication systems. Meanwhile, the design method has strong universality and can be easily extended to other issues.

As part of its ongoing efforts to meet the increased spectrum demand, the Federal Communications Commission (FCC) has recently opened up 150 MHz in the 3.5 GHz band for shared wireless broadband use. Access and operations in this band, aka Citizens Broadband Radio Service (CBRS), will be managed by a dynamic spectrum access system (SAS) to enable seamless spectrum sharing between secondary users (SUs) and incumbent users. Despite its benefits, SAS's design requirements, as set by FCC, present privacy risks to SUs, merely because SUs are required to share sensitive operational information (e.g., location, identity, spectrum usage) with SAS to be able to learn about spectrum availability in their vicinity. In this paper, we propose TrustSAS, a trustworthy framework for SAS that synergizes state-of-the-art cryptographic techniques with blockchain technology in an innovative way to address these privacy issues while complying with FCC's regulatory design requirements. We analyze the security of our framework and evaluate its performance through analysis, simulation and experimentation. We show that TrustSAS can offer high security guarantees with reasonable overhead, making it an ideal solution for addressing SUs' privacy issues in an operational SAS environment.

This paper describes the goals of the ATMOSPHERE project, which is a multi-institutional research and development (R&D) effort aiming at designing and implementing a framework and platform to develop, build, deploy, measure and evolve trustworthy, cloud-enabled applications. The proposed system addresses the federation of geographically distributed cloud computing providers that rely on lightweight virtualization, and provide access to heterogeneous sets of resources. In addition, the system also considers both classic trustworthiness properties from the systems community, such as dependability and security, and from the machine learning community, such as fairness and transparency. We present the architecture that has been proposed to address these challenges and discuss some preliminary results.

Intelligent software applications are becoming ubiquitous and pervasive affecting various aspects of our lives and livelihoods. At the same time, the risks to which these systems expose the organizations and end users are growing dramatically. Trustworthiness of software applications is becoming a paramount necessity. Trust is to be regarded as a first-class citizen in the total product life cycle and should be addressed across all stages of software development. Trust can be looked at from two facets: one at an algorithmic level (e.g., bias-free, discrimination-aware, explainable and interpretable techniques) and the other at a process level by making development processes more transparent, auditable, and adhering to regulations and best practices. In this paper, we address the latter and propose a blockchain enabled governance framework for building trustworthy software. Our framework supports the recording, monitoring, and analysis of various activities throughout the application development life cycle thereby bringing in transparency and auditability. It facilitates the specification of regulations and best practices and verifies for its adherence raising alerts of non-compliance and prescribes remedial measures.

Failure to detect malware at its very inception leaves room for it to post significant threat and cost to cyber security for not only individuals, organizations but also the society and nation. However, the rapid growth in volume and diversity of malware renders conventional detection techniques that utilize feature extraction and comparison insufficient, making it very difficult for well-trained network administrators to identify malware, not to mention regular users of internet. Challenges in malware detection is exacerbated since complexity in the type and structure also increase dramatically in these years to include source code, binary file, shell script, Perl script, instructions, settings and others. Such increased complexity offers a premium on misjudgment. In order to increase malware detection efficiency and accuracy under large volume and multiple types of malware, this research adopts Convolutional Neural Networks (CNN), one of the most successful deep learning techniques. The experiment shows an accuracy rate of over 90% in identifying malicious and benign codes. The experiment also presents that CNN is effective with detecting source code and binary code, it can further identify malware that is embedded into benign code, leaving malware no place to hide. This research proposes a feasible solution for network administrators to efficiently identify malware at the very inception in the severe network environment nowadays, so that information technology personnel can take protective actions in a timely manner and make preparations for potential follow-up cyber-attacks.

We consider transmissions of secure messages over a burst erasure wiretap channel under decoding delay constraint. For block codes we introduce and study delay optimal secure burst erasure correcting (DO-SBE) codes that provide perfect security and recover a burst of erasures of a limited length with minimum possible delay. Our explicit constructions of DO-SBE block codes achieve maximum secrecy rate. We also consider a model of a burst erasure wiretap channel for the streaming setup, where in any sliding window of a given size, in a stream of encoded source packets, the eavesdropper is able to observe packets in an interval of a given size. For that model we obtain an information theoretic upper bound on the secrecy rate for delay optimal streaming codes. We show that our block codes can be used for construction of delay optimal burst erasure correcting streaming codes which provide perfect security and meet the upper bound for a certain class of code parameters.

Cyber security is a vital performance metric for networks. Wiretap attacks belong to passive attacks. It commonly exists in wired or wireless networks, where an eavesdropper steals useful information by wiretapping messages being shipped on network links. It seriously damages the confidentiality of communications. This paper proposed a secure network coding system architecture against wiretap attacks. It combines and collaborates network coding with cryptography technology. Some illustrating examples are given to show how to build such a system and prove its defense is much stronger than a system with a single defender, either network coding or cryptography. Moreover, the system is characterized by flexibility, simplicity, and easy to set up. Finally, it could be used for both deterministic and random network coding system.

With the rapid development of the contemporary society, wide use of smart phone and vehicle sensing devices brings a huge influence on the extensive data collection. Network coding can only provide weak security privacy protection. Aiming at weak secure feature of network coding, this paper proposes an information transfer mechanism, Weak Security Network Coding with Homomorphic Encryption (HE-WSNC), and it is integrated into routing policy. In this mechanism, a movement model is designed, which allows information transmission process under Wi-Fi and Bluetooth environment rather than consuming 4G data flow. Not only does this application reduce the cost, but also improve reliability of data transmission. Moreover, it attracts more users to participate.