Browsing by Author "Creutzburg,R."

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Citation Count: 0
A Comprehensive Review of Open Source Intelligence in Intelligent Transportation Systems
(Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, Mert İlhan; Ecevit,M.I.; Daǧ,H.; Creutzburg,R.
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 real-world 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. © 2024 IEEE.
Citation Count: 0
Improving COVID-19 Detection: Leveraging Convolutional Neural Networks in Chest X-Ray Imaging
(SPIE, 2024) Jamil,M.; Chukwu,I.J.; Creutzburg,R.
The global impact of the COVID-19 pandemic has significantly disrupted healthcare systems w orldwide. Amidst challenges, there is a crucial demand for efficient me thodologies to ex pedite di sease de tection. Th is research underscores the potential of Deep Neural Networks in enhancing pandemic management over the past five years. Focusing on Artificial Intelligence (AI) application in COVID-19 detection through X-ray imaging, this research advocates using Visual Geometry Group (VGG’16), a Convolutional Neural Network (CNN) used for image classification w ith m ultiple l ayers. T hese C NNs a ct a s c lassifier-based sy stems, tr eating im ages as structured data arrays to identify and learn patterns. Quantifying the model’s effectiveness t hrough t he a ccuracy s core, t his r esearch r eveals a 0 .90% accuracy, indicating the model’s accurate detection of COVID-19 cases in X-ray images. Additionally, the study highlights a significant a chievement w ith a l ess t han 1 0% f alse p ositive r ate, c rucial f or r eliable a nd p rompt COVID-19 diagnoses in the healthcare industry. In conclusion, this research presents an AI-driven approach, utilizing VGG’16 and convolutional neural networks to enhance the efficiency an d ac curacy of CO VID-19 de tection in X-ray imaging. The high accuracy score and low false positive rate positions this methodology as a valuable contribution, offering robust pandemic management and healthcare decision-making. © 2024 SPIE
Citation Count: 0
The Open Source Intelligence (OSINT) in the Electricity Sector: Balancing Utility and Responsibility
(Society for Imaging Science and Technology, 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag,H.; Creutzburg,R.
Critical infrastructure is the backbone of modern societies, and protecting this infrastructure is essential to ensure the stability of societies and economies. The electricity sector is one of the most critical infrastructures, and any disruption can have significant consequences. The threat landscape in this sector is constantly evolving. With the increasing sophistication of cyber-attacks and other threats, it has become essential to use innovative technologies to identify and mitigate them. Open Source Intelligence (OSINT) technologies have emerged and offer valuable tools for identifying and mitigating these threats. This article presents an in-depth overview of OSINT technologies and their applications in the protection of critical infrastructure, with an emphasis on the electricity sector. It discusses the vulnerabilities of the electricity sector, the types of OSINT technologies, and the benefits they provide. Case studies of successful applications of OSINT technologies in the electricity sector are presented to illustrate their effectiveness. This article also examines organizations’ challenges in implementing OSINT technologies, including technological, legal, and financial challenges. Finally, the article concludes by offering recommendations for successfully implementing OSINT technologies to protect critical infrastructure, particularly in the electricity sector. The insights offered in this article will be helpful for policymakers, security professionals, and anyone interested in protecting critical infrastructure. © 2024, Society for Imaging Science and Technology.
Citation Count: 1
Open-Source Intelligence (OSINT) Investigation in Facebook
(Society for Imaging Science and Technology, 2023) Ecevit, Mert İlhan; Bhosale,C.; Pervez,M.H.; Naqvi,N.Z.; Ecevit,M.I.; Schwarz,K.; Creutzburg,R.
OSINT has come a long way now. It is still developing ideas, and many investigations are yet to happen shortly. The essential requirement for all OSINT investigations is information that is valuable data from an excellent source. This paper discusses various tools and methodologies related to Facebook data collection and analyzing part of the collected data. At the end of the paper, the reader will get a deep and clear insight into the available techniques, tools, and descriptions of those tools present to scrape the data out of the Facebook platform and the types of investigations and analyses that the gathered data can do. It should be noted that the paper presents the status of the OSINT Facebook investigation possibilities of November 2022. © 2023, Society for Imaging Science and Technology.
Securereg: Combining Nlp and Mlp for Enhanced Detection of Malicious Domain Name Registrations
(Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, Mert İlhan; Dağ, Hasan; Dag,H.; Creutzburg,R.; 0
The escalating landscape of cyber threats, charac-terized by the registration of thousands of new domains daily for lar ge-scale Inter net attacks such as spam, phishing, and drive-by downloads, underscor es the imperati ve for innovative detection methodologies. This paper introduces a cutting-edge approach for identifying suspicious domains at the onset of the registration process. The accompanying data pipeline generates crucial featur es by comparing new domains to register ed do-mains, emphasizing the crucial similarity score. The proposed system analyzes semantic and numerical attrib utes by leveraging a novel combination of Natural Language Processing (NLP) techniques, including a pretrained CANINE model and Multilayer Perceptr on (MLP) models, providing a robust solution for early threat detection. This integrated Pretrained NLP (CANINE) + MLP model showcases the outstanding perf ormance, surpassing both individual pretrained NLP models and standalone MLP models. With an PI score of 84.86% and an accuracy of 84.95%on the SecureReg dataset, it effecti vely detects malicious domain registrations. The finding demonstrate the effecti veness of the integrated appr oach and contrib ute to the ongoing efforts to develop proactive strategies to mitigate the risks associated with illicit online activities through the ear ly identificatio of suspicious domain registrations. © 2024 IEEE.
Citation Count: 0
Towards Better Cyber Security Consciousness: The Ease and Danger of OSINT Tools in Exposing Critical Infrastructure Vulnerabilities
(Institute of Electrical and Electronics Engineers Inc., 2023) Ecevit, Mert İlhan; Dağ, Hasan; Naqvi,N.Z.; Creutzburg,R.; Dag,H.
This article explores open-source intelligence (OS-INT) to identify the vulnerabilities and loopholes in power grid systems, focusing on an electrical distribution company operating in Turkey. The study emphasizes the potential risks of sharing publicly available information on social media accounts, websites, reports, and press releases which most companies overlook. It highlights that individuals or adversaries can exploit this information to harm companies and countries that may not be fully aware of these vulnerabilities. OSINT tools can efficiently gather interpretable data on a company, which companies unknowingly share. By refining the collected data, the study aims to understand the technologies used, their software versions, and any associated vulnerabilities. Web scraping tools extract data from the company's website, which may contain critical information about updates, ongoing systems, and technologies. The article provides a comprehensive understanding of the potential risks and vulnerabilities associated with sharing sensitive information and the various OSINT tools and techniques that can be used to identify and address these vulnerabilities. The importance of vigilance against the potential harm that remote or unrelated individuals can inflict using OSINT capabilitiesis underscored. This study shows how easy it is to detect vulnerabilities in a critical infrastructure system using OSINT tools. © 2023 IEEE.