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Auflistung nach Autor:in "Kirdan, Erkan"

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  • Konferenzbeitrag
    Optimizing OPC UA Deployments on Node.js through Advanced Logging Techniques
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Kirdan, Erkan; Schindler, Josef; Waedt, Karl
    This paper explores the value and benefits of implementing advanced logging techniques within OPC UA deployments in Node.js. OPC UA is a leading protocol for interoperable and secure data exchange in industrial automation and IoT, among other complex data communication systems. Adopting sophisticated logging strategies can optimize its deployments on Node.js. The paper uses a case study to demonstrate the real-world impact of integrating robust logging solutions into OPC UA deployments. It underscores how such practices can improve system reliability, increase debugging efficiency, enhance security, and understand system performance. This valuable insight aids developers and system administrators in managing and maintaining complex OPC UA deployments, reinforcing the critical role of a well- implemented logging strategy. By analyzing a specific instance of an OPC UA server-client pair implemented in Node.js, the paper invites a broader discussion around the optimization strategies that could further strengthen the robustness and security of OPC UA systems. It aims to open avenues for more research, encouraging a continuous drive towards more efficient and secure industrial automation and data communication systems.
  • Konferenzbeitrag
    Security challenges and best practices for resilient IIoT Networks: Network Segmentation
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Yatagha, Romarick; Waedt, Karl; Schindler, Josef; Kirdan, Erkan
    The surging prominence of the Industrial Internet of Things (IIoT) introduces both unique prospects and complex issues for industrial control systems, notably within the cybersecurity sphere. Cybersecurity concerns are particularly acute for smart factories, entities that leverage IIoT capabilities like networked sensors and machine learning to streamline production. The heterogeneous devices from diverse manufacturers and vast interconnected networks heighten their susceptibility to cyber threats. This paper examines the contemporary cybersecurity landscape within smart factories, pinpointing current vulnerabilities and imminent threats. Drawing on this analysis, we put forth a suite of best practices and strategic measures to fortify IIoT networks, including but not limited to network segmentation and stringent access controls. We pay specific attention to network segmentation, a technique used to break down a computer network into manageable subnetworks, thus mitigating the risk of attacks. We propose an innovative network segmentation policy that leverages clustering, an unsupervised learning algorithm. This algorithm classifies network traffic into distinct categories based on, but not limited to, source and destination IP addresses, employed protocol, and packet size. This data-driven classification simplifies network segmentation and configuration, minimizing their complexity. The paper also underlines the critical role of employee training and awareness in establishing robust security practices, particularly for the design, integration, and deployment of IIoT devices and edge computing. Our findings offer actionable insights for industrial control systems operators and cybersecurity professionals, empowering them to fortify their IIoT networks against cyber threats effectively.