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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 An Augmented Reality Application for Computer Engineering Curriculum(2017) Şimşek, Mehmet; Toklu, Sinan; Özsaraç, Hamza; Zavrak, Sultan; Başer, Ekrem; Takgil, Büşra; Kanbur, ZaferToday, smart phones and tablet PCs have a huge application area due to their capabilities and ease of use. One of these application areas is education. Especially, supportive technologies have brought big innovations on teaching abstract concepts to the students. One of these technologies is Augmented Reality (AR) which moves graphic&animation usage one step towards. In this study, we shared our experiments on the usage of AR in computer engineering curriculum and presented the application that developed with using AR for supporting the abstract concepts of Discrete Mathematical Structures courseÖğe Binary apple tree: A game approach to tree traversal algorithms(2012) Şentürk, Zehra Karapınar; Şentürk, Arafat; Zavrak, Sultan; Kara, Resul; Erdoğmuş, PakizeThe computer science students mostly face with the difficulties in learning the topics of algorithms courses. Only listening the topic from the teacher or just writing makes the learning volatile. Instead of listening or writing, if there is something visual, it would be more permanent to learn because visuality increases the learning potential and the time for learning is minimized. The adversities of classical education techniques were intended to be eliminated in this study via computer games which are becoming more and more popular in this age. An educational convenience is provided for the subject of tree traversal algorithms. Tree traversal algorithms are one of the basic and confused concepts in algorithms and programming courses in computer science. A game called "binary apple tree" was established to teach and learn the subject easier. © 2012 IEEE.Öğe Development and implementation of an analysis tool for direct current electrical circuits(Wiley, 2021) Pehlivan, Huseyin; Atalar, Celal; Zavrak, SultanElectrical circuits constitute the core of many courses at the undergraduate level in electrical and electronics engineering. For most undergraduate students, learning and analyzing such circuits are a difficult process. A significant drawback of Simulation Program with Integrated Circuit Emphasis (SPICE)-based simulation tools in terms of e-learning is that they only generate circuit simulation outputs, such as the current and voltage of electrical elements contained in a particular circuit. The users are not provided with the detailed information about the steps that are followed to obtain the related outputs. This study describes the development of a new software tool, called ECDAT (a shorthand for the Electrical Circuit Description and Analysis Tool), which can serve as a practical component of electrical circuits courses. The developed tool currently analyzes simple direct electric circuits in a similar way as the existing circuit analysis and simulation ones, and it produces an output document that includes the certain equations and intermediate calculations, using the well-known circuit laws differently from the previous works. Another contribution of the study is that, unlike the modified nodal analysis method used in SPICE-based circuit analysis programs, it employs a graph analysis method for circuit analysis.Öğe Email spam detection using hierarchical attention hybrid deep learning method(Pergamon-Elsevier Science Ltd, 2023) Zavrak, Sultan; Yilmaz, SeyhmusEmail is one of the most widely used ways to communicate, with millions of people and businesses relying on it to communicate and share knowledge and information on a daily basis. Nevertheless, the rise in email users has occurred a dramatic increase in spam emails in recent years. Considering the escalating number of spam emails, it has become crucial to devise effective strategies for spam detection. To tackle this challenge, this article proposes a novel technique for email spam detection that is based on a combination of convolutional neural networks, gated recurrent units, and attention mechanisms. During system training, the network is selectively focused on necessary parts of the email text. The usage of convolution layers to extract more meaningful, abstract, and generalizable features by hierarchical representation is the major contribution of this study. Additionally, this contribution incorporates cross-dataset evaluation, which enables the generation of more independent performance results from the model's training dataset. According to cross-dataset evaluation results, the proposed technique advances the results of the present attention-based techniques by utilizing temporal convolutions, which give us more flexible receptive field sizes are utilized. The suggested technique's findings are compared to those of state-of-the-art models and show that our approach outperforms them.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]Öğe A game to test pointers: Path finding(2012) Şentürk, Zehra Karapınar; Zavrak, Sultan; Şentürk, Arafat; Kara, Resul; Erdoğmuş, PakizePointers are one of the most difficult to understand topics in programming courses. Since the topic is some virtual, the students of computer science face with difficulties in understanding. They hardly imagine the addresses of memory cells, their contents, and the pointers pointing to those memory cells. To test the knowledge and to reinforce the explanations done on the lecture hours, we have intended to create a game related to pointers. This game was constituted with the purpose of visualizing the concept of pointers into the students' brains and so to increase the understanding of the subject. The students will not only listen the topic from the teacher or just write, but in an evaluation test, they will also see what is going on in the memory cells of their computer. This will make the teaching more and more permanent. © 2012 IEEE.Öğe IMPLEMENTATION OF E-LEARNING CIRCUIT ANALYSIS SOFTWARE FOR SIMPLE DIRECT CURRENT ELECTRICAL CIRCUITS(Iated-Int Assoc Technology Education A& Development, 2013) Zavrak, Sultan; Pehlivan, Hüseyin; Kara, ResulElectric circuits form the principal subject of many undergraduate courses in electrical and electronic engineering. For most higher education students, it is a difficult process to learn the analysis and solution of such circuits. In this study, we present the description of an education program that can analyze simple direct current (DC) electrical circuits and generate instructive documents with all their intermediate solution steps. The circuits currently comprise resistors, independent current and voltage sources only. Besides, the program is equipped with some useful features such as the calculation of equivalent resistance, the construction and solution of Thevenin equivalent circuits and the automatic generation of electrical circuits.Öğe Leakage detection and localization on water transportation pipelines: a multi-label classification approach(Springer London Ltd, 2017) Kayaalp, Fatih; Zengin, Ahmet; Kara, Resul; Zavrak, SultanOne of the main problems of water transportation pipelines is leak which can cause water resources loss, possible human injuries, and damages to the environment. There are many studies in the literature focusing on detection and localization of leaks in the water pipeline systems. In this study, we have designed a wireless sensor network-based real-time monitoring system to detect and locate the leaks on multiple positions on water pipelines by using pressure data. At first, the pressure data are collected from wireless pressure sensor nodes. After that, unlike from the previous works in the literature, both the detection and localization of leakages are carried out by using multi-label learning methods. We have used three multi-label classification methods which are RAkELd, BRkNN, and BR with SVM. After the evaluation and comparison of the methods with each other, we observe that the RAkELd method performs best on almost all measures with the accuracy ratio of 98%. As a result, multi-label classification methods can be used on the detection and localization of the leaks in the pipeline systems successfully.
