Meet our projects
A Universal Cyber Security Toolkit for Health-Care Industry
Horizon 2020 / SU-TDS-02-2018
SPHINX aims to introduce a health tailored Universal Cyber Security Toolkit, thus enhancing the cyber protection of the Health and Care IT Ecosystem and ensuring patients’ data privacy and integrity
Hospitals and Care Centres are prime targets of cyber criminals, especially concerning data theft, ransomware, man-in-the-middle and phishing. This reflects the need of Healthcare Institutions for a Holistic Cyber Security vulnerability assessment toolkit, that will be able to proactively assess and mitigate cyber-security threats known or unknown, imposed by devices and services within a corporate ecosystem.
SPHINX aims to introduce a Universal Cyber Security Toolkit, thus enhancing the cyber protection of Health IT Ecosystem and ensuring the patient data privacy and integrity. It will also provide an automated zero-touch device and service verification toolkit that will be easily adapted or embedded on existing, medical, clinical or health available infrastructures.
The SPHINX Toolkit will be validated through pan-European demonstrations in three different scenarios at different countries (Romania, Portugal and Greece). Hospitals, care centres and device manufacturers participating in the project’s pilots will deploy and evaluate the solution at business as usual and emergency situations across various use case scenarios.
A novel holistic approach for hardware trojan detection powered by deep learning
Horizon 2020 - Grant agreement No 777222
Hardware trojans impact our everyday life and may even cause life threatening situations. Unlike other errors and malfunctions, trojans are inserted deliberately. Apart from insider attacks, the economically driven outsourcing of production steps to third party contractors enlarges the attack surface dramatically. Today, there is no single tool that can provide a holistic approach for hardware trojan detection covering both pre-silicon verification and post-silicon testing. HERO takes a significant step forward by developing an artificial intelligence empowered approach that will be capable of identifying vulnerable regions in IC designs as well as perform post-silicon validation to cover trojans of different types and sizes under large parameter variations. The deployment of HERO technology is expected to bring a breakthrough in numerous IC-related sectors including cell phones, digital cameras, microelectromechanical systems, photonics and bioelectronic devices.
in collaboration with TUM
A Fully Automated Identification of Skin Morphology in Raster-Scan Optoacoustic Mesoscopy
Identification of morphological characteristics of skin lesions is of vital importance in diagnosing diseases with dermatological manifestations. This task is often performed manually or in an automated way based on intensity level. Recently, ultra-broadband raster-scan optoacoustic mesoscopy (UWB-RSOM) was developed to offer unique cross-sectional optical imaging of the skin. In collaboration with TUM and Prof. Ntziachristos, AIDEAS is working on a machine learning approach to enable, for the first time, automated identification of skin layers in UWB-RSOM data. The proposed method, termed SkinSeg, has been applied to coronal UWB-RSOM images obtained from 12 human participants. SkinSeg is a multi-step methodology that integrates data processing and transformation, feature extraction, feature selection and classification. Various image features and learning models were tested for their suitability at discriminating skin layers including traditional machine learning along with more advanced deep learning algorithms. An SVM-based post-processing approach was finally applied to further improve the classification outputs. Random forest and convolutional neural networks provided promising results at a level of approximately 85%. The application of the proposed post-processing technique was proved to be effective in terms of both testing accuracy and 3D visualization of classification maps. SkinSeg demonstrated unique potential in identifying skin layers. The proposed method may facilitate clinical evaluation, monitoring and diagnosis of diseases linked to skin inflammation,
Application of machine intelligence for osteoarthritis diagnosis and prediction
Osteoarthritis is the most common form of arthritis in the knee that comes with a variation in symptoms’ intensity, frequency and pattern. Knee OA (KOA) is often diagnosed using invasive and expensive methods that can measure changes in joint morphology and function. Early and accurate identification of significant risk factors in clinical data is of vital importance in diagnosing KOA. AIDEAS is working on a machine intelligence approach to enable automated, non-invasive identification of risk factors from self-reported clinical data about joint symptoms, disability, function and general health. Different machine learning and deep learning algorithms have been tested and compared in terms of multiple criteria e.g. accuracy, per class accuracy and execution time. The results suggest that machine intelligence and especially deep learning may facilitate clinical evaluation, monitoring and even prediction of knee osteoarthritis.