Digital transformation of existing processes using technologies such as big data, advanced data analytics, machine learning, automation and cloud computing will enable continuous performance improvements within the operational sphere. The application of the technology will link the physical and digital world, providing a digital model of physical assets and processes. It will represent the evergreen, wholly integrated digital asset model - from reservoir to export pipeline. The Brage operations team identified processes with the highest potential for digital transformations during an initial opportunity framing workshop. Based on pain points and business needs, the clear emphasis is in the areas of production & well performance optimization, high-spec live-data visualization, as well as the entire flow of information from database integration up to dashboarding. Strong reliance on an improved data infrastructure and IT/OT performance will require close cooperation with the IT/OT team. New solutions will be aligned with and integrated into already identified global solutions. The improved acquisition of data, together with the integration of already existing ‘Digital Twin’ technologies will enable the first redesign of work processes. Prioritization of processes to be transformed will be mandated by their business impact as well as possibility to scale to other Wintershall Dea assets too. Focus areas, such as Slugging, Digital Production Engineer, Intelligent Maintenance, Dashboarding and Planning/Scheduling, were defined as more and more ideas evolved. Individual processes such as water injection and scaling surveillance were made more time efficient and transparent. The vision is to bring all processes together into a customizable and collaborative dashboarding solution. Intended completion of the first phase is Q4 2019. The following phase will be the execution of a larger scoped technology implementation that will be defined in detail by then.
Attacks by Advanced Persistent Threats (APTs) have been shown to be difficult to detect using traditional signature-and anomaly-based intrusion detection approaches. Deception techniques such as decoy objects, often called honey items, may be deployed for intrusion detection and attack analysis, providing an alternative to detect APT behaviours. This work explores the use of honey items to classify intrusion interactions, differentiating automated attacks from those which need some human reasoning and interaction towards APT detection. Multiple decoy items are deployed on honeypots in a virtual honey network, some as breadcrumbs to detect indications of a structured manual attack. Monitoring functionality was created around Elastic Stack with a Kibana dashboard created to display interactions with various honey items. APT type manual intrusions are simulated by an experienced pentesting practitioner carrying out simulated attacks. Interactions with honey items are evaluated in order to determine their suitability for discriminating between automated tools and direct human intervention. The results show that it is possible to differentiate automatic attacks from manual structured attacks; from the nature of the interactions with the honey items. The use of honey items found in the honeypot, such as in later parts of a structured attack, have been shown to be successful in classification of manual attacks, as well as towards providing an indication of severity of the attacks
This a preprint and has not been peer reviewed. Data may be preliminary.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.