Edeh, NatashaYatagha, RomarickMejri, OumaymaWaedt, KarlKlein, MaikeKrupka, DanielWinter, CorneliaGergeleit, MartinMartin, Ludger2024-10-212024-10-212024978-3-88579-746-3https://dl.gi.de/handle/20.500.12116/45141This research investigates the growing threat of stego-malware in Industrial Control Systems (ICS), where attackers utilize steganography to embed malicious code covertly. Such attacks pose significant challenges due to their ability to evade traditional detection methods. The study reviews current cybersecurity frameworks and detection techniques, highlighting their strengths and limitations against stego-malware. It explores various detection approaches, including signature-based, anomaly-based, and AI/ML-based methods, assessing their effectiveness within the context of ISO/IEC 27001 and IEC 62443 standards. Case studies such as Havex and Industroyer underscore the real-world impact of stego-malware on ICS infrastructure. The research advocates for enhanced integration of AI and machine learning to bolster steganalysis capabilities, and proposes improvements to existing cybersecurity frameworks to address steganographic threats more effectively. By bridging gaps in current knowledge, this study contributes to advancing cybersecurity measures tailored to protect critical ICS environments against evolving cyber threats.enStegomalwareICSSteganographyAIMLSteganalysisText/Conference Paper10.18420/inf2024_1641617-5468