Filters

2019 - 201922020 - 20222

More filters

20224
Reset filters

Settings

Author: Henze, Martin × Type: BookPart ×

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Hi Doppelgänger: Towards Detecting Manipulation in News Comments
    (New York City : Association for Computing Machinery, 2019) Pennekamp, Jan; Henze, Martin; Hohlfeld, Oliver; Panchenko, Andriy
    Public opinion manipulation is a serious threat to society, potentially influencing elections and the political situation even in established democracies. The prevalence of online media and the opportunity for users to express opinions in comments magnifies the problem. Governments, organizations, and companies can exploit this situation for biasing opinions. Typically, they deploy a large number of pseudonyms to create an impression of a crowd that supports specific opinions. Side channel information (such as IP addresses or identities of browsers) often allows a reliable detection of pseudonyms managed by a single person. However, while spoofing and anonymizing data that links these accounts is simple, a linking without is very challenging. In this paper, we evaluate whether stylometric features allow a detection of such doppelgängers within comment sections on news articles. To this end, we adapt a state-of-the-art doppelgänger detector to work on small texts (such as comments) and apply it on three popular news sites in two languages. Our results reveal that detecting potential doppelgängers based on linguistics is a promising approach even when no reliable side channel information is available. Preliminary results following an application in the wild shows indications for doppelgängers in real world data sets.
  • Thumbnail Image
    Item
    A Case for Integrated Data Processing in Large-Scale Cyber-Physical Systems
    (Maui, Hawaii : HICSS, 2019) Glebke, René; Henze, Martin; Wehrle, Klaus; Niemietz, Philipp; Trauth, Daniel; Mattfeld, Patrick; Bergs, Thomas; Bui, Tung X.
    Large-scale cyber-physical systems such as manufacturing lines generate vast amounts of data to guarantee precise control of their machinery. Visions such as the Industrial Internet of Things aim at making this data available also to computation systems outside the lines to increase productivity and product quality. However, rising amounts and complexities of data and control decisions push existing infrastructure for data transmission, storage, and processing to its limits. In this paper, we exemplarily study a fine blanking line which can produce up to 6.2 Gbit/s worth of data to showcase the extreme requirements found in modern manufacturing. We consequently propose integrated data processing which keeps inherently local and small-scale tasks close to the processes while at the same time centralizing tasks relying on more complex decision procedures and remote data sources. Our approach thus allows for both maintaining control of field-level processes and leveraging the benefits of “big data” applications.
  • Thumbnail Image
    Item
    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.