Browsing by Author "Kilinc,S."

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Citation Count: 0
Advancing Anomaly Detection in Time Series Data: A Knowledge Distillation Approach with LSTM Model
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Camlidere,B.; Yildiz,E.; Guler,A.K.; Alsan,H.F.; Arsan,T.
This paper focuses on enhancing anomaly detection in time series data using deep learning techniques. Particularly, it investigates the integration of knowledge distillation with LSTM-based models for improved precision, efficiency, and interpretability. The study outlines objectives such as dataset preprocessing, developing a novel LSTM-knowledge distillation framework, incorporating Grafana, InfluxDB, Flask API with Docker, performance assessment, and practical implications. Results highlight the efficacy of knowledge distillation in enhancing student model performance. The proposed approach enhances anomaly detection, offering a viable solution for real-world applications. © 2023 IEEE.
Citation Count: 0
Network Traffic Anomaly Detection Using Quantile Regression with Tolerance
(Institute of Electrical and Electronics Engineers Inc., 2023) Arsan, Taner; Guler,A.K.; Yildiz,E.; Kilinc,S.; Camlidere,B.; Arsan,T.
Network traffic anomaly detection describes a time series anomaly detection problem where a sudden increase or decrease (called spikes) in network traffic is predicted. Data is modeled with the trend and heteroscedastic noise component. Traditional autoregressive models struggle to capture data changes effectively, making anomaly detection difficult. Our approach is to generate upper and lower limits by using quantile regression. We use a deep learning based multilayer perceptron model to predict five data quantiles 1, 25, 50, 75, and 99. The upper and lower limits are calculated as differences between the quantile-1 and quantile-99. Any data that is outside these limits are considered as an anomaly. We also add tolerance to these limits to add flexibility to anomaly detection. Anomalies and non-anomalies are labeled to get a binary classification task. Anomaly detection is class imbalanced by nature; therefore, precision, recall, and F-1 score are computed to evaluate the proposed anomaly detection method. We conclude that choosing tolerance is a tradeoff between false alarms and missing anomaly detections. © 2023 IEEE.