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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 fourth paper "Feature Dynamic Deep Learning Approach for DDoS Mitigation within the ISP Domain" by Ili Ko, Desmond Chambers, and Enda Barrett, addresses DDoS attacks caused by compromised IoT devices. The paper introduces a stacked self-organizing map (SOM), which is a feature dynamic deep learning approach that utilizes Netflow data collected by the Internet service provider (ISP) to combat the dynamic nature of novel DDoS attacks. By exploring these regularities in IoT-related indicators such as destination port, type of service (ToS), and transmission control protocol (TCP) window size, the activities of attacking hosts associated with well-known classes of malware programs can be discovered. [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]