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Öğe Anomaly-Based Intrusion Detection From Network Flow Features Using Variational Autoencoder(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Zavrak, Sultan; Iskefiyeli, MuratThe rapid increase in network traffic has recently led to the importance of flow-based intrusion detection systems processing a small amount of traffic data. Furthermore, anomaly-based methods, which can identify unknown attacks are also integrated into these systems. In this study, the focus is concentrated on the detection of anomalous network traffic (or intrusions) from flow-based data using unsupervised deep learning methods with semi-supervised learning approach. More specifically, Autoencoder and Variational Autoencoder methods were employed to identify unknown attacks using flow features. In the experiments carried out, the flow-based features extracted out of network traffic data, including typical and different types of attacks, were used. The Receiver Operating Characteristics (ROC) and the area under ROC curve, resulting from these methods were calculated and compared with One-Class Support Vector Machine. The ROC curves were examined in detail to analyze the performance of the methods in various threshold values. The experimental results show that Variational Autoencoder performs, for the most part, better than Autoencoder and One-Class Support Vector Machine.Öğe Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach(Springer London Ltd, 2023) Zavrak, Sultan; Iskefiyeli, MuratIn this study, we present and implement the SAnDet (SDN anomaly detector) architecture, an anomaly-based intrusion detection system designed to take advantage of the capabilities offered by software-defined networking (SDN) architecture, as a controller application. The SAnDet system is composed of three modules: statistics collection, anomaly detection, and anomaly prevention. In particular, we utilize replicator neural networks (RNN), which is a specialized variant of the autoencoder, and the LSTM-based encoder-decoder (EncDecAD) method, which is a special type of long short-term memory (LSTM) network that has demonstrated a strong performance on data series particularly, to identify unknown attacks using flow features collected from OpenFlow switches. In our experiments, we utilize flow-based features extracted from network traffic data containing various types of attacks as input to our models in the form of time series. We evaluate the performance of our methods using the accuracy and area under the receiver operating characteristic curve (AUC) metrics. Our experimental results demonstrate that EncDecAD outperforms RNN and that our approach offers several benefits over previously conducted research.Öğe Flow-based intrusion detection on software-defined networks: a multivariate time series anomaly detection approach (vol 35, pg 175, 2023)(Springer London Ltd, 2023) Zavrak, Sultan; Iskefiyeli, Murat[No abstract available]
