Browsing by Author "Creutzburg, Reiner"

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    Article
    Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
    (MDPI, 2025) Jamil, Mahnoor; Trpcheska, Hristina Mihajloska; Popovska-Mitrovikj, Aleksandra; Dimitrova, Vesna; Creutzburg, Reiner; 01. Kadir Has University
    Image-based spam poses a significant challenge for traditional text-based filters, as malicious content is often embedded within images to bypass keyword detection techniques. This study investigates and compares the performance of six machine learning models-ResNet50, XGBoost, Logistic Regression, LightGBM, Support Vector Machine (SVM), and VGG16-using a curated dataset containing 678 legitimate (ham) and 520 spam images. The novelty of this research lies in its comprehensive side-by-side evaluation of diverse models on the same dataset, using standardized dataset preprocessing, balanced data splits, and validation techniques. Model performance was assessed using evaluation metrics such as accuracy, receiver operating characteristic (ROC) curve, precision, recall, and area under the curve (AUC). The results indicate that ResNet50 achieved the highest classification performance, followed closely by XGBoost and Logistic Regression. This work provides practical insights into the strengths and limitations of traditional, ensemble-based, and deep learning models for image-based spam detection. The findings can support the development of more effective and generalizable spam filtering solutions in multimedia-rich communication platforms.
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    A Comprehensive Review of Open Source Intelligence in Intelligent Transportation Systems
    (Ieee Computer Soc, 2024) Ucar, Bilal Emir; Ecevit, Mert Ilhan; Dag, Hasan; Creutzburg, Reiner; Management Information Systems; 03. Faculty of Economics, Administrative and Social Sciences; 01. Kadir Has University
    This paper offers an insightful review of Open Source Intelligence (OSINT) within Intelligent Transportation Systems (ITS), emphasizing its heightened importance amidst the digital and connected evolution of the transportation sector. It highlights the integration of technologies like IoT and SCADA systems, which, while beneficial, introduce new cyber vulnerabilities. Focusing on the utilization of OSINT for surveillance, threat detection, and risk assessment, the study evaluates key tools such as Shodan and Aircrack-ng, addressing their roles in enhancing transportation system security. The paper also tackles challenges in OSINT application, from data reliability to ethical and legal considerations, stressing the need for a balance between technological advancement and privacy protection. Through realworld case studies, the paper illustrates OSINT's practical applications in scenarios like maritime security and military surveillance. Conclusively, it underscores the necessity for continuous dialogue among experts to navigate the complexities of OSINT in transportation, particularly as technology evolves and data volumes increase.