Lisansüstü Eğitim Enstitüsü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12469/77

Browse

Browsing Lisansüstü Eğitim Enstitüsü by Publication Index "WoS"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Master Thesis
    Citation - WoS: 39
    Citation - Scopus: 58
    An Ensemble of Pre-Trained Transformer Models for Imbalanced Multiclass Malware Classification
    (Kadir Has Üniversitesi, 2022) Demirkıran, Ferhat; Dağ, Hasan
    Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Hence, malware identification enables security researchers and incident responders to take precautions against malware and accelerate mitigation. API call sequences made by malware are widely utilized features by machine and deep learning models for malware classification as these sequences represent the behavior of malware. However, traditional ma chine and deep learning models remain incapable of capturing sequence relation ships among API calls. Unlike traditional machine and deep learning models, the transformer-based models process the sequences in whole and learn relationships among API calls due to multi-head attention mechanisms and positional embed dings. Our experiments demonstrate that the transformer model with one trans former block layer surpass the performance of the widely used base architecture, LSTM. Moreover, BERT or CANINE, the pre-trained transformer models, out performs in classifying highly imbalanced malware families according to evaluation metrics: F1-score and AUC score. Furthermore, our proposed bagging-based ran dom transformer forest (RTF) model, an ensemble of BERT or CANINE, reaches the state-of-the-art evaluation scores on the three out of four datasets, specifically it captures a state-of-the-art F1-score of 0.6149 on one of the commonly used bench mark dataset.
  • Thumbnail Image
    Article
    Citation - WoS: 24
    Citation - Scopus: 38
    A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
    (Mdpi, 2020) Karadayı, Yıldız; Aydın, Mehmet Nafiz; Öğrenci, Arif Selçuk
    Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.