Ecevit, Mert İlhan

Profile Picture
Profile URL
https://hdl.handle.net/20.500.12469/6110
Name Variants
Ecevit, M. İ.
Ecevit, Mert İlhan
M. İ. Ecevit
Ecevit,Mert Ilhan
Mert İlhan ECEVIT
Mert Ilhan, Ecevit
E., Mert İlhan
Ecevit,M.I.
Ecevit, M.
ECEVIT, Mert İlhan
Mert İlhan Ecevit
M. Ecevit
MERT İLHAN ECEVIT
Ecevit,M.İ.
Ecevit, Mert Ilhan
E., Mert Ilhan
ECEVIT, MERT İLHAN
E.,Mert Ilhan
Ecevit, MERT İLHAN
Ecevit, Mert İLhan
Ecevit, M.I.
Job Title
Araş. Gör.
Email Address
[email protected]
Main Affiliation
Management Information Systems
Status
Current Staff
Website
ORCID ID
Scopus Author ID
57964038500
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

5

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

2

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

2

Research Products
Documents

9

Citations

8

h-index

2

Go to Scopus profile
This researcher does not have a WoS ID.
Scholarly Output

10

Articles

0

Views / Downloads

121/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

0

Scopus Citation Count

8

WoS h-index

0

Scopus h-index

2

Patents

0

Projects

0

WoS Citations per Publication

0.00

Scopus Citations per Publication

0.80

Open Access Source

3

Supervised Theses

0

Google Analytics Visitor Traffic

JournalCount
UBMK 2024 - Proceedings: 9th International Conference on Computer Science and Engineering -- 9th International Conference on Computer Science and Engineering, UBMK 2024 -- 26 October 2024 through 28 October 2024 -- Antalya -- 2049062
International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 -- 4th IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2024 -- 25 July 2024 through 27 July 2024 -- Sydney -- 2032041
International Conference on Intelligent Environments (IE) -- JUN 17-20, 2024 -- Ljubljana, SLOVENIA1
IS and T International Symposium on Electronic Imaging Science and Technology -- IS and T International Symposium on Electronic Imaging 2024: Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2024 -- 21 January 2024 through 25 January 2024 -- San Francisco -- 1969651
2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024 -- 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024 -- 29 July 2024 through 31 July 2024 -- London -- 2018771
Current Page: 1 / 2

Scopus Quartile Distribution

Competency Cloud

GCRIS Competency Cloud

Filters

202472023220221
Reset filters

Settings

Scholarly Output Search Results

Now showing 1 - 10 of 10
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Open-Source Intelligence (OSINT) Investigation in Facebook
    (Society for Imaging Science and Technology, 2023) Narasimhan,P.K.; 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.
  • Thumbnail Image
    Conference Object
    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
    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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Resource-Efficient Ensemble Learning for Edge Iiot Network Security Against Osint-Based Attacks
    (Institute of Electrical and Electronics Engineers Inc., 2024) Ecevit, M.I.; Çukur, Z.; Izgün, M.A.; Ui Ain, N.; Daǧ, H.
    The rise of Edge IIoT networks has transformed industries by enabling real-time data processing, but these networks face significant c ybersecurity risks, particularly from OSINT-based attacks. This paper presents a resource-efficient ensemble learning framework designed to detect such attacks in Edge IIoT environments. The framework integrates machine learning models, including RandomForest, K-Nearest Neighbors, and Logistic Regression, optimized with Principal Component Analysis (PCA) to reduce data dimensionality and computational overhead. GridSearchCV and StratifiedKFold cross-validation were employed to fine-tune the models, resulting in high detection accuracy. This approach ensures robust and efficient security for resource-constrained Edge IIoT networks. © 2024 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Reviewing the Effects of Spatial Features on Price Prediction for Real Estate Market: Istanbul Case
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ecevit, M.I.; Erdem, Z.; Dag, H.
    In the real estate market, spatial features play a crucial role in determining property appraisals and prices. When spatial features are considered, classification techniques have been rarely studied compared to regression, which is commonly used for price prediction. This study reviews spatial features' effects on predicting the house price ranges for real estate in Istanbul, Turkey, in the classification context. Spatial features are generated and extracted by geocoding the address information from the original data set. This geocoding and feature extraction is another challenge in this research. The experiments compare the performance of Decision Trees (DT), Random Forests (RF), and Logistic Regression (LR) classifier models on the data set with and without spatial features. The prediction models are evaluated based on classification metrics such as accuracy, precision, recall, and F1-Score. We additionally examine the ROC curve of each classifier. The test results show that the RF model outperforms the DT and LR models. It is observed that spatial features, when incorporated with non-spatial features, significantly improve the prediction performance of the models for the house price ranges. It is considered that the results can contribute to making decisions more accurately for the appraisal in the real estate industry. © 2022 IEEE.
  • Thumbnail Image
    Conference Object
    Transfer Learning for Phishing Detection: Screenshot-Based Website Classification
    (Institute of Electrical and Electronics Engineers Inc., 2024) Çolhak, F.; Ecevit, M.I.; Daǧ, H.
    Phishing remains a significant threat in the evolving cybersecurity landscape as phishing websites become increasingly similar to legitimate websites, complicating detection using traditional methods. This study explores AI-based solutions for screenshot-based phishing detection, utilizing the MTLP dataset and applying transfer learning with pretrained models (DenseNet, ResNet, EfficientNet, Inception, MobileNet, VGG) using the timm library. The study also discusses challenges related to phishing datasets and compares publicly available datasets, highlighting MTLP Dataset's strengths. DenseNetBlur121D was identified as the top-performing model, achieving an accuracy of 95.28%, a recall of 95.38%, a precision of 93.42%, and an F1 score of 94.39% when applied to the entire MTLP dataset. Both the model code and dataset are publicly available, providing a valuable resource for further research and development in this domain. © 2024 IEEE.
  • Thumbnail Image
    Conference Object
    A Comprehensive Review of Open Source Intelligence in Intelligent Transportation Systems
    (Institute of Electrical and Electronics Engineers Inc., 2024) Uçar,B.E.; 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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Securereg: Combining Nlp and Mlp for Enhanced Detection of Malicious Domain Name Registrations
    (Institute of Electrical and Electronics Engineers Inc., 2024) Colhak,F.; Ecevit,M.I.; Dag,H.; Creutzburg,R.
    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.
  • Thumbnail Image
    Conference Object
    The Open Source Intelligence (osint) in the Electricity Sector: Balancing Utility and Responsibility
    (Society for Imaging Science and Technology, 2024) Ecevit,M.I.; Pervez,M.H.; 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.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 2
    Comparing Deep Neural Networks and Machine Learning for Detecting Malicious Domain Name Registrations
    (Institute of Electrical and Electronics Engineers Inc., 2024) Çolhak,F.; Ecevit,M.İ.; Daǧ,H.; Creutzburg,R.
    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% Fl-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, © 2024 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    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) Pervez,M.H.; Ecevit,M.I.; 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.