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AI4CYBER is co-organising the IEEE Workshop on Electrical Power and Energy System Security, Privacy and Resilience (EPES\u2013 SPR) co-located to the IEEE CSR – Cybersecurity and Resilience<\/a>.<\/p>\n In anticipating the critical issues of EPES\/SG, both academia and industry have developed appropriate countermeasures, considering the advances in the Artificial Intelligence (AI) and the networking domains. AI and especially Machine Learning (ML) and Deep Learning (DL) allow the implementation of detection mechanisms capable of discriminating malicious behaviors as well as zero-day vulnerabilities. Emerging solutions in this sector include Security Information and Event Management (SIEM) systems and Intrusion Detection and Prevention Systems (IDPS). Other emblematic technologies that can mitigate or even prevent cyberattacks are honeypots, Software-Defined Networking (SDN), Network Function Virtualization (NFV) and intentional islanding.<\/p>\n The workshop focus is going beyond this state of practice and is requesting prospective authors are encouraged to submit previously unpublished contributions from a broad range of topics, which include but are not limited to the following:<\/p>\n \u203a Intrusion\/anomaly detection and mitigation in EPES\/SG For more details the full Call for Papers is available at Flyer_Workshop_on_Electrical_Power.pdf (ieee-csr.org)<\/a><\/p>\n The workshop is also supported by the EU Horizon projects: ELECTRON<\/a>, TERMINET<\/a> and DYNABIC<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" AI4CYBER is co-organising the IEEE Workshop on Electrical Power and Energy System Security, Privacy and Resilience (EPES\u2013 SPR) co-located to the IEEE CSR – Cybersecurity and Resilience. In anticipating the critical issues of EPES\/SG, both academia and industry have developed appropriate countermeasures, considering the advances in the Artificial Intelligence (AI) and the networking domains. AI […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[9],"tags":[],"class_list":["post-1408","post","type-post","status-publish","format-standard","hentry","category-news"],"_links":{"self":[{"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=\/wp\/v2\/posts\/1408","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=1408"}],"version-history":[{"count":2,"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=\/wp\/v2\/posts\/1408\/revisions"}],"predecessor-version":[{"id":1410,"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=\/wp\/v2\/posts\/1408\/revisions\/1410"}],"wp:attachment":[{"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1408"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1408"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ai4cyber.eu\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\n\u203a SDN\/NFV-based architectures for resilient EPES\/SG
\n\u203a EPES\/SG honeypots and honeynets
\n\u203a SIEM systems in EPES\/SG
\n\u203a Self-healing in EPES\/SG
\n\u203a Federated learning solutions for anomaly and cyberattack detection in EPES\/SG
\n\u203a Security management and risk assessment in EPES\/SG
\n\u203a Threat modelling and vulnerability analysis in EPES
\n\u203a Security management and risk assessment in EPES\/SG
\n\u203a Emerging privacy-preserving mechanisms and techniques in EPES\/SG<\/p>\n