Browsing by Author "Ecevit, M.I."

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Citation Count: 0
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.
Citation Count: 0
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.
Citation Count: 0
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.