| Title | AI Threat Detection and Response on Smart Networks |
|---|---|
| ID_Doc | 44451 |
| Authors | Dermetzis, K; Iliadis, L |
| Title | AI Threat Detection and Response on Smart Networks |
| Year | 2021 |
| Published | |
| Abstract | The main goal of smart cities is to dynamically optimize the quality of life, through the application of information and communication technologies (ICT). The involved networks, require a continuous increase in data exchange, in order to intelligently control services and in particular, mechanisms that activate a higher degree of automation in the city. As many critical services are interconnected, the need for cyber security is increasing, in order to ensure data exchange protection, privacy, and better health and safety services for all citizens. The security and evolution of smart cities is based on the security of their smart networks which are activated by specific automation mechanisms, such as the SCADA networks and the pre-eminent automation systems. This paper presents the AnomaTS, an advanced Machine Learning system, for anomaly detection in sensors of SCADA networks, taking into account the temporal state of their mechanisms. |
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