Biblio

Engineering drawings such as Piping and Instrumentation Diagrams contain a vast amount of text data which is essential to identify shapes, pipeline activities, tags, amongst others. These diagrams are often stored in undigitised format, such as paper copy, meaning the information contained within the diagrams is not readily accessible to inspect and use for further data analytics. In this paper, we make use of the benefits of recent deep learning advances by selecting models for both text detection and text recognition, and apply them to the digitisation of text from within real world complex engineering diagrams. Results show that 90% of text strings were detected including vertical text strings, however certain non text diagram elements were detected as text. Text strings were obtained by the text recognition method for 86% of detected text instances. The findings show that whilst the chosen Deep Learning methods were able to detect and recognise text which occurred in simple scenarios, more complex representations of text including those text strings located in close proximity to other drawing elements were highlighted as a remaining challenge.

Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called “Deepfake” produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector.

Nowadays, Fog Computing gets more attention due to its characteristics. Fog computing provides more advantages in related to apply with the latest technology. On the other hand, there is an issue about the data security over processing of data. Fog Computing encounters many security challenges like false data injection, violating privacy in edge devices and integrity of data, etc. An encryption scheme called Homomorphic Encryption (HME) technique is used to protect the data from the various security threats. This homomorphic encryption scheme allows doing manipulation over the encrypted data without decrypting it. This scheme can be implemented in many systems with various crypto-algorithms. This homomorphic encryption technique is mainly used to retain the privacy and to process the stored encrypted data on a remote server. This paper addresses the terminologies of Fog Computing, work flow and properties of the homomorphic encryption algorithm, followed by exploring the application of homomorphic encryption in various public key cryptosystems such as RSA and Pailier. It focuses on various homomorphic encryption schemes implemented by various researchers such as Brakerski-Gentry-Vaikuntanathan model, Improved Homomorphic Cryptosystem, Upgraded ElGamal based Algebric homomorphic encryption scheme, In-Direct rapid homomorphic encryption scheme which provides integrity of data.

In the Ubiquitous Electric Internet of Things (UEIoT), the terminals are very easy to be accessed and attacked by attackers due to the lack of effective monitoring and safe isolation methods. Therefore, in the implementation of UEIoT, the security protection of terminals is particularly important. Therefore, this paper proposes a dual-system design scheme for terminal active immunity based on trusted computing. In this scheme, the terminal node in UEIoT is composed of two parts: computing part and trusted protection part. The computing component and the trusted protection component are logically independent of each other, forming a trusted computing active immune dual-system structure with both computing and protection functions. The Trusted Network Connection extends the trusted state of the terminal to the network, thus providing a solution for terminal secure access in the UEIoT.

Security maintenance of Supervisory Control and Data Acquisition (SCADA) systems has been a point of interest during recent years. Numerous research works have been dedicated to the design of intrusion detection systems for securing SCADA communications. Nevertheless, these data-driven techniques are usually dependant on the quality of the monitored data. In this work, we propose a novel feature selection approach, called MSFS, to tackle undesirable quality of data caused by feature redundancy. In contrast to most feature selection techniques, the proposed method models each class in a different subspace, where it is optimally discriminated. This has been accomplished by resorting to ensemble learning, which enables the usage of multiple feature sets in the same feature space. The proposed method is then utilized to perform intrusion detection in smaller subspaces, which brings about efficiency and accuracy. Moreover, a comparative study is performed on a number of advanced feature selection algorithms. Furthermore, a dataset obtained from the SCADA system of a gas pipeline is employed to enable a realistic simulation. The results indicate the proposed approach extensively improves the detection performance in terms of classification accuracy and standard deviation.

This project addresses a classic employment law question in Canada and elsewhere using machine learning approach: how do we know whether a worker is an employee or an independent contractor? This is a central issue for self-represented litigants insofar as these two legal categories entail very different rights and employment protections. In this interdisciplinary research study, we collaborated with the Conflict Analytics Lab to develop machine learning models aimed at determining whether a worker is an employee or an independent contractor. We present a number of supervised learning models including a neural network model that we implemented using data labeled by law researchers and compared the accuracy of the models. Our neural network model achieved an accuracy rate of 91.5%. A critical discussion follows to identify the key features in the data that influence the accuracy of our models and provide insights about the case outcomes.

This article presents the design and development of a relay switch (RS) to handle electrical loads up to 20A using WiFi technology. The hardware design and the implementation methodology are explained, both for the power supply and for the wireless communication that are embedded in the same small printed circuit board. In the same way, the design of the implemented firmware to operate the developed RS is shown. An ESP-12E module is used to achieve wireless communication of the RS, which can be manipulated through a web page using an MQTT protocol or via and iOS or Arduino app. The developed RS presents at least three differentiators in relation to other similar devices on the market: it can handle a higher electrical load, has a design in accordance with national and international security standards and can use different cybersecurity strategies for wireless communication with the purpose of safe and reliable use. Experimental results using a lamp and a single-phase motor as electrical loads demonstrate an excellent performance and reliability of the developed relay switch.

This chapter presents a game‐theoretic model to analyze attack–defense scenarios that use fake nodes (computing devices) for deception under consideration of the system deploying defense resources to protect individual nodes in a cost‐effective manner. The developed model has important applications in the Internet of Battlefield Things (IoBT). Our game‐theoretic model illustrates how the concept of the Nash equilibrium can be used by the defender to intelligently choose which nodes should be used for performing a computation task while deceiving the attacker into expending resources for attacking fake nodes. Our model considers the fact that defense resources may become compromised under an attack and suggests that the defender, in a probabilistic manner, may utilize unprotected nodes for performing a computation while the attacker is deceived into attacking a node with defense resources installed. The chapter also presents a deception‐based strategy to protect a target node that can be accessed via a tree network. Numerical results provide insights into the strategic deception techniques presented in this chapter.

Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.

DeepFake using Generative Adversarial Networks (GANs) tampered videos reveals a new challenge in today's life. With the inception of GANs, generating high-quality fake videos becomes much easier and in a very realistic manner. Therefore, the development of efficient tools that can automatically detect these fake videos is of paramount importance. The proposed DeepFake detection method takes the advantage of the fact that current DeepFake generation algorithms cannot generate face images with varied resolutions, it is only able to generate new faces with a limited size and resolution, a further distortion and blur is needed to match and fit the fake face with the background and surrounding context in the source video. This transformation causes exclusive blur inconsistency between the generated face and its background in the outcome DeepFake videos, in turn, these artifacts can be effectively spotted by examining the edge pixels in the wavelet domain of the faces in each frame compared to the rest of the frame. A blur inconsistency detection scheme relied on the type of edge and the analysis of its sharpness using Haar wavelet transform as shown in this paper, by using this feature, it can determine if the face region in a video has been blurred or not and to what extent it has been blurred. Thus will lead to the detection of DeepFake videos. The effectiveness of the proposed scheme is demonstrated in the experimental results where the “UADFV” dataset has been used for the evaluation, a very successful detection rate with more than 90.5% was gained.

For the task of natural language processing (NLP), Word embedding technology has a certain impact on the accuracy of deep neural network algorithms. Considering that the current word embedding method cannot realize the coexistence of words and phrases in the same vector space. Therefore, we propose an enhanced word embedding (EWE) method. Before completing the word embedding, this method introduces a unique sentence reorganization technology to rewrite all the sentences in the original training corpus. Then, all the original corpus and the reorganized corpus are merged together as the training corpus of the distributed word embedding model, so as to realize the coexistence problem of words and phrases in the same vector space. We carried out experiment to demonstrate the effectiveness of the EWE algorithm on three classic benchmark datasets. The results show that the EWE method can significantly improve the classification performance of the CNN model.

The increased use of cloud services and its various security and privacy challenges such as identity theft, data breach, data integrity and data confidentiality has made trust management, which is one of the most multifaceted aspect in cloud computing, inevitable. The growing reputation of cloud computing technology makes it immensely important to be acquainted with the meaning of trust in the cloud, as well as identify how the customer and the cloud service providers establish that trust. The traditional trust management mechanisms represent a static trust relationship which falls deficit while meeting up the dynamic requirement of cloud services. In this paper, a conceptual zero trust strategy for the cloud environment has been proposed. The model offers a conceptual typology of perceptions and philosophies for establishing trust in cloud services. Further, importance of trust establishment and challenges of trust in cloud computing have also been explored and discussed.

Augmented reality (AR) has been identified as a key technology to enhance worker utility in the context of increasing automation of repeatable procedures. AR can achieve this by assisting the user in performing complex and frequently changing procedures. Crucial to the success of procedure assistance AR applications is user acceptability, which can be measured by user quality of experience (QoE). An active research topic in QoE is the identification of implicit metrics that can be used to continuously infer user QoE during a multimedia experience. A user's QoE is linked to their affective state. Affective state is reflected in facial expressions. Emotions shown in micro facial expressions resemble those expressed in normal expressions but are distinguished from them by their brief duration. The novelty of this work lies in the evaluation of micro facial expressions as a continuous QoE metric by means of correlation analysis to the more traditional and accepted post-experience self-reporting. In this work, an optimal Rubik's Cube solver AR application was used as a proof of concept for complex procedure assistance. This was compared with a paper-based procedure assistance control. QoE expressed by affect in normal and micro facial expressions was evaluated through correlation analysis with post-experience reports. The results show that the AR application yielded higher task success rates and shorter task durations. Micro facial expressions reflecting disgust correlated moderately to the questionnaire responses for instruction disinterest in the AR application.

We present a scalable architecture based on a trained filter bank for input pre-processing and a recurrent neural network (RNN) for the detection of atrial fibrillation in electrocardiogram (ECG) signals, with the focus on enabling a very efficient hardware implementation as application-specific integrated circuit (ASIC). Our already very efficient base architecture is further improved by replacing the RNN with a delta-encoded gated recurrent unit (GRU) and adding a confidence measure (CM) for terminating the computation as early as possible. With these optimizations, we demonstrate a reduction of the processing load of 58 % on an internal dataset while still achieving near state-of-the-art classification results on the Physionet ECG dataset with only 1202 parameters.

Privacy preservation is one of the greatest concerns when data is shared between different organizations. On the one hand, releasing data for research purposes is inevitable. On the other hand, sharing this data can jeopardize users' privacy. An effective solution, for the sharing organizations, is to use anonymization techniques to hide the users' sensitive information. One of the most popular anonymization techniques is k-Anonymization in which any data record is indistinguishable from at least k-1 other records. However, one of the fundamental challenges in choosing the value of k is the trade-off between achieving a higher privacy and the information loss associated with the anonymization. In this paper, the problem of choosing the optimal anonymization level for k-anonymization, under possible attacks, is studied when multiple organizations share their data to a common platform. In particular, two common types of attacks are considered that can target the k-anonymization technique. To this end, a novel game-theoretic framework is proposed to model the interactions between the sharing organizations and the attacker. The problem is formulated as a static game and its different Nash equilibria solutions are analytically derived. Simulation results show that the proposed framework can significantly improve the utility of the sharing organizations through optimizing the choice of k value.

Supervisor synthesis enables the design of supervisory controllers for large cyber-physical systems, with high guarantees for functionality and safety. The complexity of the synthesis problem, however, increases exponentially with the number of system components in the cyber-physical system and the number of models of this system, often resulting in lengthy or even unsolvable synthesis procedures. In this paper, a new method is proposed for reducing the model of the system before synthesis to decrease the required computational time and effort. The method consists of three steps for model reduction, that are mainly based on symmetry in dependency graphs of the system. Dependency graphs visualize the components in the system and the relations between these components. The proposed method is applied in a case study on the design of a supervisory controller for a road tunnel. In this case study, the model reduction steps are described, and results are shown on the effectiveness of model reduction in terms of model size and synthesis time.

Radio frequency identification (RFID) is widespread and still necessary in many important applications. However, and in various significant cases, the use of this technology faces multiple security issues that must be addressed. This is mainly related to the use of RFID tags (transponders) which are electronic components communicating wirelessly, and hence they are vulnerable to multiple attacks through several means. In this work, an extensive fault analysis is performed on a tag architecture in order to evaluate its hardness. Tens of millions of single-bit upset (SBU) and multiple-bit upset (MBU) faults are emulated randomly on this tag architecture using an FPGA-based emulation platform. The emulated faults are classified under five groups according to faults effect on the tag behaviour. The obtained results show the faults effect variation in function of the number of MBU affected bits. The interpretation of this variation allows evaluating the tag robustness. The proposed approach represents an efficient mean that permits to study tag architectures at the design level and evaluating their robustness and vulnerability to fault attacks.

Deepfake imagery that contains altered faces has become a threat to online content. Current anti-deepfake approaches usually do so by detecting image anomalies, such as visible artifacts or inconsistencies. However, with deepfake advances, these visual artifacts are becoming harder to detect. In this paper, we show that one can use biometric eyebrow matching as a tool to detect manipulated faces. Our method could provide an 0.88 AUC and 20.7% EER for deepfake detection when applied to the highest quality deepfake dataset, Celeb-DF.

Cyber-Physical Systems (CPS) such as intelligent connected vehicles, smart farming and smart logistics are constantly generating tons of data and requiring real-time data processing capabilities. Therefore, Edge Computing which provisions computing resources close to the End Devices from the network edge is becoming the ideal platform for CPS. However, it also brings many issues and one of the most prominent challenges is how to ensure the development of trustworthy smart services given the dynamic and distributed nature of Edge Computing. To tackle this challenge, this paper proposes a novel Federated Learning based Edge Computing platform for CPS, named “FengHuoLun”. Specifically, based on FengHuoLun, we can: 1) implement smart services where machine learning models are trained in a trusted Federated Learning framework; 2) assure the trustworthiness of smart services where CPS behaviours are tested and monitored using the Federated Learning framework. As a work in progress, we have presented an overview of the FengHuoLun platform and also some preliminary studies on its key components, and finally discussed some important future research directions.

In this work, a new human-computer assistive technology gadget designed for people with impairments is evaluated. The developed human-in-the-loop interface device has an embedded assistance controller and can replace the traditional mouse, gamepad and keyboard, enabling human-computer hands-free full access. This work is concerned with the assistive device performance characterization aspects. Based on the experiments carried out, the human-computer performance improvement with the embedded controller is analysed in detail. Results show that adding the human-in-the-loop assistance controller improves human-computer hands-free skills, which is an innovative contribution for the replacement of computer interfaces that depend on the human hands.

The industrial Internet of Things (IIoT) can facilitate industrial upgrading, intelligent manufacturing, and lean production. Industrial control system (ICS) is a vital support mechanism for many key infrastructures in the IIoT. However, natural defects in the ICS network security mechanism and the susceptibility of the programmable logic controller (PLC) program to malicious attack pose a threat to the safety of national infrastructure equipment. To improve the security of the underlying equipment in ICS, a model checking method based on timed automata is proposed in this work, which can effectively model the control process and accurately simulate the system state when incorporating time factors. Formal analysis of the ICS and PLC is then conducted to formulate malware detection rules which can constrain the normal behavior of the system. The model checking tool UPPAAL is then used to verify the properties by detecting whether there is an exception in the system and determine the behavior of malware through counter-examples. The chemical reaction control system in Tennessee-Eastman process is taken as an example to carry out modeling, characterization, and verification, and can effectively detect multiple patterns of malware and propose relevant security policy recommendations.

Memory corruption vulnerabilities, mainly present in C and C++ applications, may enable attackers to maliciously take control over the program running on a target machine by forcing it to execute an unintended sequence of instructions present in memory. This is the principle of modern Code-Reuse Attacks (CRAs) and of famous attack paradigms as Return-Oriented Programming (ROP) and Jump-Oriented Programming (JOP). Control-Flow Integrity (CFI) is a promising approach to protect against such runtime attacks. Recently, many CFI-based solutions have been proposed, resorting to both hardware and software implementations. However, many of these solutions are hardly applicable to microcontroller systems, often very resource-limited. The paper presents a generic, portable, and lightweight CFI solution for bare-metal embedded systems, i.e., systems that execute firmware directly from their Flash memory, without any Operating System. The proposed defense mixes software and hardware instrumentation and is based on monitoring the Control-Flow Graph (CFG) with an FPGA connected to the CPU. The solution, applicable in principle to any architecture which disposes of an FPGA, forces all control-flow transfers to be compliant with the CFG, and preserves the execution context from possible corruption when entering unpredictable code such as Interrupt Services Routines (ISR).

The intelligent electronic devices widely deployed across the distribution network are inevitably making the feeder automation (FA) system more vulnerable to cyber-attacks, which would lead to disastrous socio-economic impacts. This paper proposes a three-stage game-theoretic framework that the defender allocates limited security resources to minimize the economic impacts on FA system while the attacker deploys limited attack resources to maximize the corresponding impacts. Meanwhile, the probability of successful attack is calculated based on the Bayesian attack graph, and a fault-tolerant location technique for centralized FA system is elaborately considered during analysis. The proposed game-theoretic framework is converted into a two-level zero-sum game model and solved by the particle swarm optimization (PSO) combined with a generalized reduced gradient algorithm. Finally, the proposed model is validated on distribution network for RBTS bus 2.

The paper presents the concept of applying a game theory approach in infrastructure of wireless dynamic networks to counter computer attacks. The applying of this approach will allow to create mechanism for adaptive reconfiguration of network structure in the context of implementation various types of computer attacks and to provide continuous operation of network even in conditions of destructive information impacts.

The Security available in personal computers and laptops are not possible in mobile communication, since there is no controlling software such as an operating system. The European Union General Data Protection Regulation (GDPR) will require many organisations throughout the European Union to comply with new requirements that are intended to protect their user's personal data. The responsibilities of the organizations and the penalties related to the protection of personal data of the users are proved to be both organisationally and technically challenging. Under the GDPR's 'privacy by design' and 'privacy by default' requirements, organizations need to prove that they are in control of user data and have taken steps to protect it. There are a large number of organizations that makes use of mobile devices to process personal data of their customers. GDPR mandates that the organization shall be able to manage all devices that handles sensitive data so that the company can implement group updates, restrict apps and networks, and enforce security measures. In this work, we propose a Mobile Device Management solution using the built-in frameworks of Android and iOS mobile platforms which is compatible and incorporates GDPR articles relevant to a small to medium sized organization.