Cybersecurity for SMEs: Introducing the Human Element into Socio-technical Cybersecurity Risk Assessment

Abstract: Small and medium-sized enterprises (SMEs) rarely conduct a thorough cyber-risk assessment and they may face various internal issues when attempting to set up cyber-risk strategies. In this work, we apply a user journey approach to model human behaviour and visually map SMEs’ practices and threats, along with a visualisation of the socio-technical actor network, targeted specifically at the risks highlighted in the user journey. By using a combination of cybersecurity-related visualisations, our goals are: i) to raise awareness about cybersecurity, and ii) to improve communication among IT personnel, security experts, and non-technical personnel. To achieve these goals, we combine two modelling languages: Customer Journey Modelling Language (CJML) is a visual language for modelling and visualisation of work processes in terms of user journeys. System Security Modeller (SSM) is an asset-based risk-analysis tool for socio-technical systems. By demonstrating the languages’ supplementary nature through a threat scenario and considering related theories, we believe that there is a sound basis to warrant further validation of CJML and SSM together to raise awareness and handle cyber threats in SMEs.

BY Ragnhild Halvorsrud

Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles

Abstract: Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.

BY Tiberiu Cocias
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