Person: ŞAHİNGÖZ, ÖZGÜR KORAY
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ŞAHİNGÖZ
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ÖZGÜR KORAY
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Publication Metadata only Deep Learning in Intrusion Detection Systems(2018) Demir, Önder; BAYDOĞMUŞ, GÖZDE KARATAŞ; ŞAHİNGÖZ, ÖZGÜR KORAY; 110942; 170651; 214903Publication Metadata only Deep learning based classification of malaria from slide images(2019) Kalkan, Soner Can; ŞAHİNGÖZ, ÖZGÜR KORAYAs one of the most life-threatening disease in the tropical and warmer-climate countries, Malaria affects not only animals but also humans who can be infected by only a single bite from a mosquito. Although this disease is wiped out in high-income countries, as a result of traveling people, it can even emerge in all part of the world. World Health Organization announced that more than 400,000 people are expected to die due to this illness. However, it is a curable and preventable disease, if early detection is possible. Traditionally, Pathologists diagnosed this disease manually by using microscope which is a time-consuming process in our computerized world, and this model depends on the experience of the Pathologists, which is a critical problem in rural areas. Therefore, in recent years detection of Malaria using computerized image analysis which is trained using some dynamic learning mechanism has gained increasing importance. In this paper, we proposed an image processing-based Malaria detection system which is trained by deep learning. We used relatively big data for increasing the accuracy of the system, and the reached accuracy showed that the proposed system has an outstanding classification rate that can be used in real-world detection.Publication Metadata only Machine Learning Based Phishing Detection from URIs(2017-12) Buber, Ebubekir; Demir, Önder; Diri, Banu; ŞAHİNGÖZ, ÖZGÜR KORAY; 214903Due to the rapid growth of the Internet, users change their preference from traditional shopping to the electronic commerce. Instead of bank/shop robbery, nowadays, criminals try to find their victims in the cyberspace with some specific tricks. By using the anonymous structure of the Internet, attackers set out new techniques, such as phishing, to deceive victims with the use of false websites to collect their sensitive information such as account IDs, usernames, passwords, etc. Understanding whether a web page is legitimate or phishing is a very challenging problem, due to its semantics-based attack struc ture, which mainly exploits the computer users’ vulnerabilities. Although software companies launch new anti-phishing products, which use blacklists, heuristics, visual and machine learning-based approaches, these products cannot prevent all of the phishing attacks. In this paper, a real-time anti-phishing system, which uses seven different classification algorithms and natural language processing (NLP) based features, is proposed. The system has the following distinguishing properties from other studies in the literature: language independence, use of a huge size of phishing and legitimate data, real-time execution, detection of new websites, independence from third-party services and use of feature-rich classifiers. For mea suring the performance of the system, a new dataset is constructed, and the experimental results are tested on it. According to the experimental and comparative results from the implemented classification algorithms, Random Forest algorithm with only NLP based features gives the best performance with the 97.98% accuracy rate for detection of phishing URLs.Publication Metadata only Deep learning based security management of information systems: A comparative study(2020-01) Çebi, Cem Berke; Bulut, Fatma Sena; Fırat, Hazal; ŞAHİNGÖZ, ÖZGÜR KORAY; BAYDOĞMUŞ, GÖZDE KARATAŞ; 214903In recent years, there is a growing trend of internetization which is a relatively new word for our global economy that aims to connect each market sectors (or even devices) by using the global network architecture as the Internet. Although this connectivity enables great opportunities in the marketplace, it results in many security vulnerabilities for admins of the computer networks. Firewalls and Antivirus systems are preferred as the first line of a defense mechanism; they are not sufficient to protect the systems from all type of attacks. Intrusion Detection Systems (IDSs), which can train themselves and improve their knowledge base, can be used as an extra line of the defense mechanism of the network. Due to its dynamic structure, IDSs are one of the most preferred solution models to protect the networks against attacks. Traditionally, standard machine learning methods are preferred for training the system. However, in recent years, there is a growing trend to transfer these standard machine learning-based systems to the deep learning models. Therefore, in this paper, IDSs with four different deep learning models are proposed, and their performance is compared. The experimental results showed that proposed models result in very high and acceptable accuracy rates with KDD Cup 99 Dataset.Publication Metadata only Deep learning based forecasting in stock market with big data analytics(2019) Şişmanoğlu, Gözde; Önde, Mehmet Ali; Koçer, Furkan; ŞAHİNGÖZ, ÖZGÜR KORAYIn recent years, due to the technological improvements in computers' hardware and enhancements in the machine learning techniques, there are two increasing approaches for problem-solving as the use of "Big Data" and "Parallel Processing". Especially with the emergence of Deep Learning algorithms which can be executed parallelly on multi-core computing devices such as GPUs and CPUs, lots of real-world problems are resolved with these approaches. One of the most critical application areas in the Financial Market especially sits on Stock Markets. In this area, the aim is trying to predict the future value of a specific stock by looking at its previous financial data on the exchange process in the market. In this paper, we proposed a system that uses a Deep Learning based approach for training and constructing a knowledge base on a specific stock such as "IBM". We get time series values of the stock from the New York Stock Exchange which starts from 1968 up to 2018. Experimental results showed that this approach produces very good forecasting for specific stocks.Publication Metadata only Autonomous vehicle control for Lane and vehicle tracking by using deep learning via vision(2018) Olgun, Masum Celil; Baytar, Zakir; Akpolat, Kadir Metin; ŞAHİNGÖZ, ÖZGÜR KORAYCamera-based lane detection and vehicle tracking algorithms are one of the keystones for many autonomous systems. The navigational process of those systems is mainly focused on the output of detection algorithms. However, detection algorithms for lane detection need more pre-processing time and computational effort. They are also affected by environmental conditions and must regularly be improved. In this paper machine learning techniques and computer vision algorithms are utilized for the tasks of the lane and vehicle tracking of an autonomous vehicle control scenario. With the nature of used learning algorithm, the proposed system can handle complex image problems. The vehicle, on which we implement our algorithms, can manage to carry out the following tasks autonomously; tracking the lanes, following another vehicle, and stopping in necessary conditions. For that, one of the primary purposes is image-based lane tracking methodology by using learning algorithms. Data augmentation is applied to create diversity for the dataset. Application in this methodology has been discussed. For lane tracking Convolutional Neural Network architecture which is based on NVIDIA's PilotNet is preferred. For detecting objects and vehicles, the system is trained on the faster region-based convolutional neural network (Faster R-CNN) to identify traffic light and stop sign are by Haar Cascade Classifier. All these learning models are trained on NVIDIA GTX 1070 Graphics Processing Unit (GPU) to reduce training time. Experimental results showed that the proposed system gives a favorable result to autonomously control vehicles for lane and vehicle tracking purposes by vision.Publication Metadata only Neural network based intrusion detection systems with different training functions(IEEE, 345 E 47th St, New York, Ny 10017 USA, 2018) BAYDOĞMUŞ, GÖZDE KARATAŞ; ŞAHİNGÖZ, ÖZGÜR KORAY; 110942; 214903In the last decades, due to the improvements in networking techniques and the increased use of the Internet, the digital communications entered all of the activities in the global marketplace. Parallel to these enhancements the attempts of hackers for intruding the networks are also increased. They tried to make unauthorized access to the networks for making some modifications in their data or to increase the network traffic for making a denial of service attack. Although a firewall seems as a good tool for preventing this type of attacks, intrusion detection systems (IDSs) are also preferred especially for detecting the attack within the network system. In the last few years, the performance of the IDS is increased with the help of machine learning algorithms whose effects depend on the used training/learning algorithm. Mainly it is really hard to know which learning algorithm can be the fastest one according to the problem type. The algorithm selection depends on lots of factors such as the size of data sets, number of nodes network design, the targeted error rate, the complexity of the problem, etc. In this paper, it is aimed to compare different network training function in a multi-layered artificial neural network which is designed for constructing an effective intrusion detection system. The experimental results are depicted in the paper by explaining the efficiency of the algorithms according to their true-positive detection rates and speed of the execution.Publication Metadata only Intrusion Detection Systems with GPU-Accelerated Deep Neural Networks and Effect of the Depth(2018) Reis, Buminhan; Kaya, Semi Berk; BAYDOĞMUŞ, GÖZDE KARATAŞ; ŞAHİNGÖZ, ÖZGÜR KORAY; 110942; 214903With the extended use of the Internet, which connects millions of computers across the world, there is a growing number and types of intrusions which complicate ensuring the security of information and computers. Although Firewalls and rule/signature base Intrusion Detection Systems (IDSs) are used as the first line of the defense of networks, they cannot be sufficient for detecting the zero-day type attacks, which are not previously encountered. For this type of attacks, Anomaly-Based Intrusion detection systems arise as an acceptable solution which models the normal communication behavior of the network and identifies the others as a suspicious transaction. To classify the normal behavior, usage of neural networks and machine learning approaches are accepted as powerful solutions. However, due to the lack of computation power, generally single hidden layer approach is preferred. With the enhancement of the parallel computation technology, especially in Graphics Processing Units (GPUs), it will be easy to implement a multi-layer approach in Deep Neural Network concept which has a great deal of attention within Deep Learning approach. Therefore, better accuracy rate could be reached. In this paper, we aimed to implement a Deep Neural Network-based Intrusion Detection System. Moreover, we also study the performance of the proposed model in binary classification with a different number of layers, neurons and parameters. Additionally, the acceleration of the GPU usage is also measured and presented with a comparison. To measure the performance of the proposed system the NSL-KDD data set, which is a 'cleaned' data set of the KDD data set, is preferred. The experimental results showed that the proposed multi-layer Deep Neural Network model produces an acceptable performance in its classification with a high accuracy rate with the design of a 64x32 hidden layer structure depending on the data set NSL-KDD.