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search.filters.applied.f.access: openAccess × Author: Wolsing, Konrad × Has files: Yes × Subject: Konferenzschrift ×

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    PowerDuck: A GOOSE Data Set of Cyberattacks in Substations
    (New York City : ACM, 2022-08-08) Zemanek, Sven; Hacker, Immanuel; Wolsing, Konrad; Wagner, Eric; Henze, Martin; Serror, Martin
    Power grids worldwide are increasingly victims of cyberattacks, where attackers can cause immense damage to critical infrastructure. The growing digitalization and networking in power grids combined with insufficient protection against cyberattacks further exacerbate this trend. Hence, security engineers and researchers must counter these new risks by continuously improving security measures. Data sets of real network traffic during cyberattacks play a decisive role in analyzing and understanding such attacks. Therefore, this paper presents PowerDuck, a publicly available security data set containing network traces of GOOSE communication in a physical substation testbed. The data set includes recordings of various scenarios with and without the presence of attacks. Furthermore, all network packets originating from the attacker are clearly labeled to facilitate their identification. We thus envision PowerDuck improving and complementing existing data sets of substations, which are often generated synthetically, thus enhancing the security of power grids.
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    IPAL: Breaking up Silos of Protocol-dependent and Domain-specific Industrial Intrusion Detection Systems
    (New York City : Association for Computing Machinery, 2022-10-26) Wolsing, Konrad; Wagner, Eric; Saillard, Antoine; Henze, Martin
    The increasing interconnection of industrial networks exposes them to an ever-growing risk of cyber attacks. To reveal such attacks early and prevent any damage, industrial intrusion detection searches for anomalies in otherwise predictable communication or process behavior. However, current efforts mostly focus on specific domains and protocols, leading to a research landscape broken up into isolated silos. Thus, existing approaches cannot be applied to other industries that would equally benefit from powerful detection. To better understand this issue, we survey 53 detection systems and find no fundamental reason for their narrow focus. Although they are often coupled to specific industrial protocols in practice, many approaches could generalize to new industrial scenarios in theory. To unlock this potential, we propose IPAL, our industrial protocol abstraction layer, to decouple intrusion detection from domain-specific industrial protocols. After proving IPAL's correctness in a reproducibility study of related work, we showcase its unique benefits by studying the generalizability of existing approaches to new datasets and conclude that they are indeed not restricted to specific domains or protocols and can perform outside their restricted silos.