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The rapid growth of the Internet of Things and the proliferation of easily compromisable IoT devices has led to a drastic increase in the occurrence of IoT-based botnet attacks. Hackers are keen on exploiting the vulnerabilities of smart devices, which are seen as easy targets often lacking robust security mechanisms. Identifying botnet activity is an active research topic and remains a challenging task due to the continuous evolution of botnet families that employ a large number of attack vectors. Traditional rule-based approaches which rely on signature matching, heuristics and behavioral profiling are always lagging one step behind the attacker, leading researchers to the development of machine and deep learning methods for the detection of compromised IoT device behaviour. In this paper, we model botnet traffic identification as an anomaly detection task, aiming at establishing a baseline of benign traffic, in order to detect unusual behavior using Netflow data. We propose a feature engineering and deep learning-based detection framework based on two Autoencoder architectures: (i) a vanilla implementation of a deep Autoencoder and (ii) GANomaly which has never been used in the context of network traffic analysis before.We validate the performance of the proposed methodology on the CICIDS2017 dataset which has been widely used for cybersecurity benchmarks and show that it is possible to induce highly accurate unsupervised learning models to detect previously unseen botnet behaviour.

Report detailing the dissemination activities performed so far

The deliverable includes amarket research and analysis including a state of art of similar initiatives on the market in order to identify M-Sec position in the market, followed by a SWOT analysis to identify barriers and drivers that will help M-Sec on setting the correct strategy to compete successfully. Additionally, a business model canvas has been developed for each of the use cases along with an individual exploitation plan to be developed by each partner both during and after the conclusion of the project in order to guarantee maximum results in terms of M-Sec exploitation. Finally, assets already generated before the project have been identified along with its owner and therefore there is not any IPR issue raised by any of the partners.&nbsp

Requirements analysis plays an important role for the whole lifecycle of the M-Sec project. It is the input for the M-Sec specification and overall architecture as well as for the validation of the final system and its evaluation against the desired functionality. In this document, we have presented the requirements analysis methodology along with the defined functional and non-functional requirements of the M-Sec system.

The report presents the initial version of the document describing the overall architecture of M-Sec and its basic building blocks. To do that, a well-established and successful methodology for the design of concrete IoT Architectures, namely the IoT Architectural Reference Model (ARM) from the IoT-A FP7 project, has been used.

Increasing automation is ongoing in all areas of transport. This raises new challenges for the design and training of Human-Machine Interfaces (HMI) for different user groups. The EU-project Drive2theFuture investigates the needs and wants of transportation users, operators, passengers and passersby to gain their acceptance and to set the ground for a sustainable market introduction of automated transport. This paper describes how HMI concepts for the transport modes road, rail, maritime and aviation in Drive2theFuture are developed and comparatively assessed in order to be able to support an educated use of automated transport. By relying on a stepwise process, adaptable HMI strategies for different user clusters and levels of automation are defined. As a universal method, a comprehensive HMI development toolkit is developed, which can be adopted as training tool to create realistic expectations and enhance acceptance among users, operators and drivers in light of the deployment of automated vehicles.

Increasing automation is ongoing in all areas of transport. This raises new challenges for the design and training of Human-Machine Interfaces (HMI) for different user groups. The EU-project Drive2theFuture investigates the needs and wants of transportation users, operators, passengers and passersby to gain their acceptance and to set the ground for a sustainable market introduction of automated transport. This paper describes how HMI concepts for the transport modes road, rail, maritime and aviation in Drive2theFuture are developed and comparatively assessed in order to be able to support an educated use of automated transport. By relying on a stepwise process, adaptable HMI strategies for different user clusters and levels of automation are defined. As a universal method, a comprehensive HMI development toolkit is developed, which can be adopted as training tool to create realistic expectations and enhance acceptance among users, operators and drivers in light of the deployment of automated vehicles.

SHIELD is a distributed cyber-security system that leverages Network Function Virtualisation for dynamically deploying virtual Network Security Functions. The security functions send network traffic’s monitoring data to a big-data store. The Data Analysis and Remediation Engine executes security analytics modules on top of monitoring data modules in order to detect threats. The security analytics heavily leverage Machine Learning algorithms for detecting anomalies and classifying threats. This paper presents the different Machine Learning algorithms and details the obtained results and the direction taken by the project with regards to its implementation, including business capabilities for the cybersecurity solution.

The report reflects the second stage of the definition of the Standardisation Survival Kit (SSK). On the basis of the various user scenarios presented in Deliverable 4.1, where each stage of the research process has been annotated according to the actual standards that are actually needed in order to fulfil the research task, we present here a systematic review of the activities that have to be carried out to provide support to researchers in using, but also contributing to, these standards. All PARTHENOSdeliverablesare available at:http://www.parthenos-project.eu/resources/projects-deliverables

Deliverable 3.3.2: Specification of tests and test groups One of the main goals of the ISISEMD project is to offer innovative ICT services to improve the quality of life of elderly persons with cognitive problems or mild dementia and their informal and formal caregivers who provide every day care for them. This will be done via integrating intelligent scalable ICT services which will be tested for a period of 12 months under realistic conditions. Offering the services could not be complete without evaluating quality of life improvement, user acceptance and user satisfaction with a representative group of the target user groups. This document is devoted to describing important aspects of services evaluation such as: who the test participants will be, inclusion and exclusion criterion, selection standards, how the test participants will be recruited, ethical considerations, etc. Test methodology, test objectives, evaluation parameters, work plan for the tests, test tasks and test scenarios are further presented.

Analysis of threat model, risks and security consideration in the future smart city context along with the technologies and methodologies that can contribute into this direction.