Democratizing a Cyber Security Toolkit for SMEs & MEs

Helping SMEs and MEs analyse, forecast and manage cyber security and data protection risks.

Making SMEs & MEs more cyber-resilient

The project will use its tools and cyber range demos to train SMEs/MEs to identify their top threats and recognize and address them with greater confidence. Results will be validated by SME/ME in four critical sectors.

Latest News

Cyber ​​Range and Training Facilitation workshop

Prosegue con grande partecipazione e interesse il progetto europeo CyberKit4SME che ci ha visti impegnati la scorsa settimana, insieme alla University of Southampton IT Innovation and University of Southampton Cyber, nel workshop “Cyber Range and Training Facilitation” presso la sede della University of Southampton. Il team di Sogei ha partecipato all’incontro finalizzato a testare i materiali che saranno […]

CyberKit4SME and HEIR projects collaboration kick-off

The technical teams recognized the potential of CyberKit4SME “Parquet Modular Encryption” technology to be adopted by HEIR. Stay tuned for more on this!

[PUZZLE] Validation Contracts Call

[ OPEN CALL ] 10.000€ grant for SMEs&MEs to test cloud-based 🛡️ #CyberSecurity system. 🗓️ Application Deadline #extended 30th June 2022! ⏳ APPLY HERE @H2020 Puzzle is looking for: 📌 10 European SMEs (adopters) to connect their systems to the PUZZLE marketplace and test the ability of the PUZZLE framework to meet their cybersecurity […]

Latest Publications

Private Data Harvesting on Alexa using Third-Party Skills

Abstract: We are currently seeing an increase in the use of voice assistants which are used for various purposes. These assistants have a wide range of inbuilt functionalities with the possibility of installing third-party applications. In this work, we will focus on analyzing and identifying vulnerabilities that are introduced by these third-party applications. In particular, we will build third-party applications (called Skills) for Alexa, the voice assistant developed by Amazon. We will analyze existing exploits, identify accessible data and propose an adversarial framework that deceives users into disclosing private information. For this purpose, we developed four different malicious Skills that harvest different pieces of private information from users. We perform a usability analysis on the Skills and feasibility analysis on the publishing pipeline for one of the Skills.

It’s Not My Problem: How Healthcare Models relate to SME Cybersecurity Awareness snippet
It’s Not My Problem: How Healthcare Models relate to SME Cybersecurity Awareness

Abstract: Small and medium enterprises (SMEs) make up a significant part of European economies. They are often described as poorly place to deal with cyber risks though because of resource constraints or commercial interests. Providing appropriate tooling would facilitate a greater appreciation of the risks and provide mitigation strategies. In a series of workshops demonstrating visualization tools for cybersecurity, constructs from healthcare models such as awareness, self-efficacy, and a willingness to engage were investigated to throw light on the likelihood that the technologies would be adopted. Although most constructs were validated, it turns out that self-efficacy could more appropriately be interpreted as a desire to understand a broader company narrative rather than empowering any individual to identify and manage cyber risk. As part of an ongoing examination of technology acceptance, this work provides further evidence that technology must be contextualized to make sense for the individual as part of the SME rather than as individual employee.

Color map of what the robot sees
Embedded Vision for Self-Driving on Forest Roads

Abstract: Forest roads in Romania are unique natural wildlife sites used for recreation by countless tourists. In order to protect and maintain these roads, we propose RovisLab AMTU (Autonomous Mobile Test Unit), which is a robotic system designed to autonomously navigate off-road terrain and inspect if any deforestation or damage occurred along tracked route. AMTU's core component is its embedded vision module, optimized for real-time environment perception. For achieving a high computation speed, we use a learning system to train a multi-task Deep Neural Network (DNN) for scene and instance segmentation of objects, while the keypoints required for simultaneous localization and mapping are calculated using a handcrafted FAST feature detector and the Lucas-Kanade tracking algorithm. Both the DNN and the handcrafted backbone are run in parallel on the GPU of an NVIDIA AGX Xavier board. We show experimental results on the test track of our research facility.