Real time DDOS and RoQ attack detection Deep packet inspection Protocol analysis Anomaly detection
Autonomous health monitoring of IoT devices Tampering attempts Abnormal payload and traffic
Novel approach: Deep and machine learning techniques Game-theoretic framework to provide recommendations for IoT service providers based on the information regarding vulnerabilities Game-theory and machine learning to create patterns and predict the evolution of malwares and botnets Intelligence gathering from: Clearnet Deep and dark web Intelligence sharing to: Organisations SOC teams Security experts LEAs CERTs
Provide trusted transaction processing and coordination between IoT devices Ensure security: Identity Data Communication Safeguarding critical files and software binaries Minimize the damage caused by tampered devices and malware as well as single points of failure Collection and storing of forensic evidence
Develop a privacy-preserving IoT device (vulnerability) profiling framework, so that any communications with high risk devices are subject to more thorough analysis.
The flat representation (on the screen) may provide synthetic information about the status of the IoT network that can be used for the monitoring phase. It will use BODY OCULUS (or similar viewers) and body movement(e.g. hand gestures) to navigate and inspect the network. Through the VR mode it will be possible to cover more network area per analyst and see more in less time; it will also allow to see panels of related information surrounding the operator in a…