Distributed Energy Resources (DERs) are growing in importance Power Systems. Battery Electrical Storage Systems (BESS) represent fundamental tools in order to balance the unpredictable power production of some Renewable Energy Sources (RES). Nevertheless, BESS are usually remotely controlled by SCADA systems, so they are prone to cyberattacks. This paper analyzes the vulnerabilities of BESS and proposes an anomaly detection algorithm that, by observing the physical behavior of the system, aims to promptly detect dangerous working conditions by exploiting the capabilities of a particular neural network architecture called the autoencoder. The results show the performance of the proposed approach with respect to the traditional One Class Support Vector Machine algorithm.
The agricultural sector increasingly makes use of automated and/or remotely-controlled machines to improve performance and reduce costs. These machines, called Smart Agricultural Machines (SAMs), integrate different information and communication technologies for monitoring and control purposes and can be remotely controlled by using proprietary protocols. This makes it difficult to assess the vulnerabilities of the system, in particular for non-proprietary-parties. SAMs are cyber-physical systems often employing private protocols and can be objects of attacks. In this context the paper proposes a framework, based on Software Defined Radio (SDR) technology, for cybersecurity verification of SAMs, in order to fill the gap in the state of the art since no technical standard specifically addresses cybersecurity in this environment; the paper describes the testbed developed and exploited to show the effectiveness in detecting vulnerabilities and assessing the SAM security, in particular focusing on the wireless communication channels, and reports the obtained results.INDEX TERMS Smart agriculture, autonomous machines, cybersecurity, software defined radio (SDR), wireless communications, penetration test.
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