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    Frameworks for smart grid cyber security analysis
    (Elsevier, 2024) Gündüz, Muhammed Zekeriya; Demirol, Doygun; Daş, Resul; Hanbay, Kazım
    This chapter provides an in-depth examination of the comprehensive framework essentials for analyzing cybersecurity in smart grid applications. It covers crucial components necessary to maintain the integrity and resilience of smart grid systems. The chapter begins with a focus on authentication and access control measures to ensure secure communication flows within the system. Robust mechanisms to authenticate and authorize users and devices accessing smart grid infrastructures are emphasized. The chapter further explores the importance of deploying pervasive attack detection and countermeasures throughout the smart grid, emphasizing proactive mitigation of cyber threats to protect critical assets. Additionally, it explores the importance of implementing basic and lightweight cryptographic functions at the node level to secure transactions and communications. The security of network protocols, from the application layer to the MAC layer, is thoroughly examined, identifying vulnerabilities and strategies for enhancing security at each protocol layer. Furthermore, the chapter highlights the utility of cyber security testbed platforms for investigating vulnerabilities within power infrastructures, providing controlled environments for simulating attacks, and evaluating security measures. Finally, a related case study of the National Institute of Standards and Technology (NIST) is integrated to offer practical insights into applying the framework in real-world scenarios, with implications for researchers, practitioners, and policymakers involved in enhancing smart grid cyber security. © 2025 Elsevier Inc. All rights reserved.
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    Graph Visualization of Cyber Threat Intelligence Data for Analysis of Cyber Attacks
    (2022) Sülü, Mücahit; Daş, Resul
    Threat intelligence enables us to make faster, more informed, data-backed security decisions and change their behavior from reactive to proactive in the fight against threat actors. Cyber threat intelligence sources include open-source intelligence, social media intelligence, human intelligence, technical intelligence, device log files, forensically acquired data or intelligence from the internet traffic, and data derived for the deep and dark web. In this study, graph visualization is discussed for the intelligible and accurate analysis of complex cyber threat intelligence data, including network attacks. The processes of collecting, cleaning, organizing, and visualizing cyber intelligence data in different formats and contents on a single platform are given step by step. Dynamic graphs play an active role in these systems, where the attack locations and targets from different points are constantly variable. Therefore, research on dynamic graph solutions and visualization in the visual analysis of cyberattacks is presented.