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Institut
Multi-level reference architecture development for digital public infrastructures based on the cloud
(2016)
Measuring IT security, compliance and data governance within small and medium-sized IT enterprises
(2020)
Companies are increasingly facing the challenges of a persistent cyber threat landscape. By means of AI, cyber attacks can be efficiently conducted more successful through offensive AI. As for cyber defense, AI can be also utilized against cyber threats (defensive AI). Due to limited resources, especially in small and medium-sized companies (SMEs), there is a need to deploy more effective defensive cyber security solutions. Precisely, the adaptation of AI-based resilient defenses must be driven forward. Therefore, the aim of this paper is to identify and evaluate AI-related use cases with a high impact potential on the cyber security level, while being applicable to SMEs at the same time. In order to reach the research goal, an extensive literature review of several online catalogs, surveys and online platforms was conducted. In conclusion, seven crucial AI-based security features were outlined that are providing a high impact potential to the security level for SMEs. Afterwards, the results are discussed and set into a broader context. Even though AI-based security solutions are providing a large range of advantages, certain challenges and barriers using AI-related security applications are addressed in the paper as well. A high need for usable state of the art AI based cyber security solution for SMEs was identified.
Dieser Beitrag entwickelt ein Konzept zur praktischen Umsetzung eines Machine- Learning Verfahrens zum Lösen von Vehicle Routing Problemen im Kontext einer nachhaltigen “Letzte-Meile”-Logistik, welches durch einen Prototyp umgesetzt und getestet wurde. Der Prototyp basiert auf dem “Reinforcement Learning”-System und verwendet als Algorithmus “REINFORCE mit Baseline”. In einer Vergleichsanalyse wurde der Prototyp mit dem bekannten Vertreter Google-OR, anhand von zwei Anwendungsszenarien, verglichen. Der Prototyp überzeugt dabei in der Laufzeit und dem Automatismus. Es konnte festgestellt werden, dass eine Verwendung von Lernenden-Systemen für das Vehicle Routing Problem sich bei nur bei einem größeren Stoppvolumen und einer erweiterten IT-Infrastruktur empfiehlt.