Auflistung nach Autor:in "Mejri, Oumayma"
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- KonferenzbeitragEnsuring trustworthy AI for sensitive infrastructure using Knowledge Representation(INFORMATIK 2024, 2024) Mejri, Oumayma; Waedt, Karl; Yatagha, Romarick; Edeh, Natasha; Sebastiao, Claudia LemosArtificial intelligence (AI) has become increasingly integrated into various aspects of society, from healthcare and finance to law enforcement and hiring processes. More recently, sensitive infrastructure such as nuclear plants is engaging AI in aspects of safety. However, these systems are not immune to biases and ethical concerns. This paper explores the role of knowledge representation in addressing ethics and fairness in AI, examining how biased or incomplete representations can lead to unfair outcomes and unreliable decision-making. It proposes strategies to mitigate these risks.
- KonferenzbeitragIntegrating Dynamic Thresholding in Anomaly Detection on Water Treatment Facilities(INFORMATIK 2024, 2024) Yatagha, Romarick; Oeztuerk, Esin; Nebebe, Betelhem; Edeh, Natasha; Mejri, OumaymaWith the growing complexity of cyber-physical systems (CPS), adaptive and robust monitoring solutions are increasingly crucial for ensuring operational reliability and safety. Anomaly detection is a critical component of monitoring systems, particularly in dynamic environments such as water management systems, where operational regimes can vary significantly over time. Traditional static thresholding techniques, which use a single fixed threshold for the entire monitoring process, are often inadequate due to their inability to adapt to changing data patterns, leading to high rates of false positives and missed detections. This paper explores the limitations of static thresholding and presents a comparative analysis with more adaptive approaches. We first discuss the use of static thresholds for each regime shift, which provides some improvement but still falls short in accommodating gradual or unexpected changes. Subsequently, we introduce a dynamic thresholding method based on the Autoregressive Integrated Moving Average (ARIMA) model. This approach continuously adjusts thresholds in real time, effectively accounting for evolving data patterns and regime shifts. Our evaluation, conducted on synthetic water level data with known anomalies, demonstrates that dynamic thresholding significantly outperforms static methods. Specifically, dynamic thresholding achieves an accuracy of 99%, precision of 78%, recall of 88%, and an F1-score of 82%, highlighting its robustness and adaptability. These results underscore the potential of dynamic thresholding techniques to enhance anomaly detection in complex, variable environments.
- KonferenzbeitragUnderstanding stegomalware in ICS: Attacks and Prevention(INFORMATIK 2024, 2024) Edeh, Natasha; Yatagha, Romarick; Mejri, Oumayma; Waedt, KarlThis 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.