Abstract.In this paper, we analyze the network attacks that can be launched against IoT gateways, identify the relevant metrics to detect them, and explain how they can be computed from packet captures. We also present the principles and design of a deep learning-based approach using dense random neural networks (RNN) for the online detection of network attacks. Empirical validation results on packet captures in which attacks were inserted show that the Dense RNN correctly detects attacks.

Abstract. We present the European research project GHOST, (Safeguarding home IoT environments with personalised real-time risk control), which challenges the traditional cyber security solutions for the IoT by proposing a novel reference architecture that is embedded in an adequately adapted smart home network gateway, and designed to be vendor-independent. GHOST proposes to lead a paradigm shift in consumer cyber security by coupling usable security with transparency and behavioural engineering.

The H2020 European research project GHOST-SafeGuarding Home IoT Environments with Personalised Realtime Risk Control-aims to deploy a highly effective security framework for IoT smart home residents through a novel reference architecture for user-centric cyber security in smart homes providing an unobtrusive and user-comprehensible solution. The aforementioned security framework leads to a transparent cyber security environment by increasing the effectiveness of the existing cyber security services and enhancing system's self-defence through disruptive software-enabled network security solutions. In this paper, GHOST security framework for IoT-based smart homes is presented. It is aiming to address the security challenges posed by several types of attacks, such as network, device and software. The effective design of the overall multilayered architecture is analysed, with particular emphasis given to the integration aspects through dynamic and re-configurable solutions and the features provided by each one of the architectural layers. Additionally, real-life trials and the associated use cases are described showcasing the competences and potential of the proposed framework.

Smart-home installations exponential growth has raised major security concerns. To this direction, the GHOST project, a European Union Horizon 2020 Research and Innovation funded project, aims to develop a reference architecture for securing smart-homes IoT ecosystem. It is required to have automated and user friendly security mechanisms embedded into smart-home environments, to protect the users’ digital well being. GHOST project aims to fulfill this requirement and one of its main functionalities is the traffic monitoring for all IoT related network protocols. In this paper, the traffic capturing and monitoring mechanism of the GHOST system, called NDFA, is presented, as the first mechanism that is able to monitor smart-home activity in a holistic way. With the help of the NDFA, we compile the GHOST-IoT-data-set, an IoT network traffic data-set, captured in a real world smart-home installation. This data-set contains traffic from multiple network interfaces with both normal real life activity and simulated abnormal functioning of the devices. The GHOST-IoT-data-set is offered to the research community as a proof of concept to demonstrate the ability of the NDFA module to process the raw network traffic from a real world smart-home installation with multiple network interfaces and IoT devices.

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