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The advent of Industry 4.0 has led to a rapid increase in cyber attacks on industrial systems and processes, particularly on Industrial Control Systems (ICS). These systems are increasingly becoming prime targets for cyber criminals and nation-states looking to extort large ransoms or cause disruptions due to their ability to cause devastating impact whenever they cease working or malfunction. Although myriads of cyber attack detection systems have been proposed and developed, these detection systems still face many challenges that are typically not found in traditional detection systems. Motivated by the need to better understand these challenges to improve current approaches, this paper aims to (1) understand the current vulnerability landscape in ICS, (2) survey current advancements of Machine Learning (ML) based methods with respect to the usage of ML base classifiers (3) provide insights to benefits and limitations of recent advancement with respect to two performance vectors; detection accuracy and attack variety. Based on our findings, we present key open challenges which will represent exciting research opportunities for the research community. [ABSTRACT FROM AUTHOR]

The conventional approaches of auditing agricultural commodities from the production and transportation level to the retailers and consumers often get obstructed by the lack of human resources, delayed response, and high frequency of location updates—resulting in poor quality and safety compliance. Its digital transformation, known as remote auditing, could address these limitations to a greater extent; however, it is still subject to diverse cyberattacks, including tampering with the video streams provided for verification. Since a detailed and concurrent forensic examination of videos during remote auditing significantly increases the computational burden on the network and infrastructure, alternative or complementary solutions should be developed. This paper outlines the technical feasibility of applying digital signatures over live video streams as a way to authenticate the source during remote auditing and thus contributes to limiting the scope of potential cyber threats. It proposes design options for realizing the authentication process with trusted computing technologies at different phases, from signing the videos to transmitting them through unreliable networks. A reference prototype is also developed based on the proposed end-to-end design to quantify the performance of trusted remote agricultural auditing in terms of the frame signing time, attack resistance, and resource overhead. [ABSTRACT FROM AUTHOR]

As a typical cyber-physical system, dispersed Phasor Measurement Units (PMUs) are networked together with advanced communication infrastructures to record the Distribution Synchrophasor (DS) which represents the states and dynamics of distribution power networks. Source information of DS is critical for many DS-based applications which is potentially vulnerable to data integrity attacks. To ensure the reliability of DS-based applications, it is imperative to efficiently authenticate the DS source locations before any DS data analytics is initiated. This letter presents a cost-effective method for accurate source identification by realising the multifractality of DS data. First, Multiscale Adaptive Multifractal Detrended Fluctuation Analysis (MSA-MFDFA) is executed to reveal the scale which possesses the most significant multifractality of the time-series DS. Subsequently, Adaptive Multifractal Interpolation (AMFI) is proposed to generate quasi high-resolution DS where unique time-frequency signatures are extracted. Such signatures are further fed into a deep learning model - deep forest for source identification. Experimental results using real-life DS of a distribution network illustrate the excellent performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Evidence shows that it is paramount for stakeholders to understand the cybersecurity of relevant organizations. However, the secrecy surrounding cyber attacks and how organizations manage their cyber resilience make it impossible for stakeholders to develop this understanding. This paper analyzes organizational cyber resilience, its contextual factors and its impact on the outcomes of cyber attacks based on publicly available data. Using the PRISMA methodology, we collated and analyzed a dataset of 1,145 publicly known cyber attacks. We conceptualize and operationalize cyber resilience from a governance perspective. Our findings indicate that organizations that suffered cyber attacks had the following cyber resilience characteristics: a relatively low level of cyber resilience reflected in the low frequency of cybersecurity roles, low reliance on cybersecurity frameworks, and relatively low strength of prevention, detection, and recovery controls. Cyber resilience is found to be associated with the sector, size, and digital intensity. Linear regression indicates that, expectedly, stronger prevention, detection, and recovery processes are related to lower breach severity and occurrence of investigations or penalties, but contrary to expectations, cybersecurity roles and frameworks are not. Furthermore, better organizational responses are associated with higher breach severity but they are not found to have an impact on the level of investigations, fines, and penalties imposed. We discuss our findings and their implications for cyber resilience regulation, future research, and sector cooperation. [ABSTRACT FROM AUTHOR]

This letter proposes a hybrid approach combining Self-Adaptive Mathematical Morphology (SAMM) and Time-Frequency (TF) techniques to authenticate the source information of Distribution Synchrophasors (DS) within near-range locations. The SAMM can adaptively regulate the synchrophasors variations which are representatives of local environmental characteristics. Subsequently, TF mapping is employed to extract informative signatures from the regulated synchrophasors variation. Finally, Random Forest Classification (RFC) is used to correlate the extracted signatures with the source information based on the derived TF mapping. Experiment results using DS collected at multiple small geographical scales validated the proposed methodology. [ABSTRACT FROM AUTHOR]

Coupling learning is designed to estimate, discover, and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand—it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this article, we develop a distributed collaborative coupling learning system, which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favorable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs. [ABSTRACT FROM AUTHOR]

General Data Protection Regulation came into effect across the European Union in May 2018 but its implications in healthcare are yet to be fully understood. The aim of this study was to identify the fronts and hot topics in research on GDPR in healthcare. We analyzed the relevant records in Scopus through bibliometric and scientometric approach and visualization techniques. A set of 155 records was obtained and processed for co-occurrence analysis of key terms and concept mapping. The number of published papers showed a steep rise in the past two years, mainly by European countries. Analysis of the abstract of the papers showed that data protection, privacy, and big data were the most frequently used terms. Three dominant research fronts of GDPR are 1) general implications of GDPR, 2) technology aspects of GDPR, and 3) GDPR in healthcare service. Blockchain and machine learning are among the remerging topics of GDPR research. [ABSTRACT FROM AUTHOR]

L. Yang et al. presented an information extraction system for purpose-aware rules of privacy policies in IoT to facilitate data privacy compliance and reduce privacy risks due to unfriendly policies. Smart cities have a unique characteristic: integration of deployment of information and communication technology (ICT) services and innovations to handle complex data in storage devices and during citywide transmission. This model aims to protect the NAL against DDoS and FC flooding by differentiating between DDoS and FC abnormal traffic and then separating DDoS botnets into Demons and Zombies to apply a suitable attack-handling methodology. [Extracted from the article]

Objective: This research uses crime scripts to understand adult retribution-style image-based sexual abuse (RS-IBSA) offender decision-making and offending in offline and online environments. We explain the crime-commission process of adult RS-IBSA and identify crime intervention points at eight crime script stages. Methods: Publicly released court transcripts of adult RS-IBSA prosecution cases (n = 18) in New Zealand from 2015 to 2018 were utilised to examine the crime-commission process of adult RS-IBSA. We analysed the court transcripts thematically at offence-level prior to constructing the crime scripts. Findings: The study identified four types of adult RS-IBSA acts including the non-consensual dissemination of a victim's intimate images, violent cyber sextortion, covert intimate photography, and unauthorised access of a victim's phone/media. From our analysis, we identified three script tracks and constructed three distinct crime scripts: (1) threats, sextortion and dissemination; (2) unauthorised access of a victim's mobile device and dissemination; and (3) covert intimate filming. We highlight areas for potential intervention for law enforcement agencies and policy makers to increase deterrence and personal security in online and offline spaces. Conclusion: Adult RS-IBSA occurs in a range of dating and domestic contexts. This study develops crime scripts for adult RS-IBSA and advances our understanding of how the Internet/smartphones/digital media translates into virtual crime scenes with opportunities for maximum harm infliction. We offer several policy implications including revising current RS-IBSA legislation and supporting law enforcement agencies with policing online and offline intimate relationship spaces through situational prevention. [ABSTRACT FROM AUTHOR]