Browsing by Author "Creutzburg, Reiner"

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Citation Count: 0
Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations
(Ieee, 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag, Hasan; Creutzburg, Reiner
This study highlights the effectiveness of deep neural network (DNN) models, particularly those integrating natural language processing (NLP) and multilayer perceptron (MLP) techniques, in detecting malicious domain registrations compared to traditional machine learning (ML) approaches. The integrated DNN models significantly outperform traditional ML models. Notably, DNN models that incorporate both textual and numeric features demonstrate enhanced detection capabilities. The utilized Canine + MLP model achieves 85.81% accuracy and an 86.46% F1-score on the MTLP Dataset. While traditional ML models offer advantages such as faster training times and smaller model sizes, their performance generally falls short compared to DNN models. This study underscores the trade-offs between computational efficiency and detection accuracy, suggesting that their superior performance often justifies the added costs despite higher resource requirements.
Citation Count: 0
A Comprehensive Review of Open Source Intelligence in Intelligent Transportation Systems
(Ieee Computer Soc, 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag, Hasan; Creutzburg, Reiner
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.