ISEEYOO aims at designing and implementing an anomaly detection system for monitoring the information collected from a network of Cyber Physical Systems (CPSs) in the communication phase.

A CPS is a system characterized by a strong interaction among networking, sensing, and control functionalities. That is, the joint exploitation and coordination between computational and physical resources. The connectivity allowed by the use of the Internet, on one hand has extended the ability to share information and on the other has made these systems prone to vulnerabilities that did not exist before. The interaction between the Internet and CPSs requires greater efforts to ensure the security of connected systems.

In this project, we design a network anomaly detection system which exploits the distributed nature of CPSs by gathering the information coming from the set of deployed devices. To detect the presence of anomalies we adopt a 2D representation of traffic data thus characterizing the multi-input information collected from the sensors in a compact form.

The proposed approach is based on deep learning techniques and on an optimized 2D representation of the network data.