Person: BULDU, ALİ
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BULDU
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ALİ
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Publication Metadata only Internet of Things (IoTs) Security: Intrusion Detection using Deep Learning(RIVER PUBLISHERS, 2021) BULDU, ALİ; Sahingoz, Ozgur Koray; Cekmez, Ugur; Buldu, AliWith the development of sensor and communication technologies, the use of connected devices in industrial applications has been common for a long time. Reduction of costs during this period and the definition of Internet of Things (IoTs) concept have expanded the application area of small connected devices to the level of end-users. This paved the way for IoT technology to provide a wide variety of application alternative and become a part of daily life. Therefore, a poorly protected IoT network is not sustainable and has a negative effect on not only devices but also the users of the system. In this case, protection mechanisms which use conventional intrusion detection approaches become inadequate. As the intruders' level of expertise increases, identification and prevention of new kinds of attacks are becoming more challenging. Thus, intelligent algorithms, which are capable of learning from the natural flow of data, are necessary to overcome possible security breaches. Many studies suggesting models on individual attack types have been successful up to a point in recent literature. However, it is seen that most of the studies aiming to detect multiple attack types cannot successfully detect all of these attacks with a single model. In this study, it is aimed to suggest an all-in-one intrusion detection mechanism for detecting multiple intrusive behaviors and given network attacks. For this aim, a custom deep neural network is designed and implemented to classify a number of different types of network attacks in IoT systems with high accuracy and F-1-score. As a test-bed for comparable results, one of the up-to-date dataset (CICIDS2017), which is highly imbalanced, is used and the reached results are compared with the recent literature. While the initial propose was successful for most of the classes in the dataset, it was noted that achievement was low in classes with a small number of samples. To overcome imbalanced data problem, we proposed a number of augmentation techniques and compared all the results. Experimental results showed that the proposed methods yield highest efficiency among observed literature.Publication Metadata only A novel thermal-based fabric defect detection technique(TAYLOR & FRANCIS LTD, 2015) BULDU, ALİ; Yildiz, Kazim; Buldu, Ali; Demetgul, Mustafa; Yildiz, ZehraDuring the fabric production process, many defects can be occurred stemming from the unevenness in spinning, weaving, finishing processes, or from the raw materials. The fabric quality control process for the detection of these defects is carried out by specialist operators. In this paper, a new method based on the use of thermal camera for detecting these defects from the textile fabric images is presented. For identification process of defective area, fabric images were obtained by a thermal camera during the fabric flow in quality control machine that was specially designed for this experiment. Defective and defect-free regions on fabric surface were determined by thermal camera due to the thermal differences. The mentioned thermal defect detection system will eliminate the worker usage for fabric quality control process, thus it will provide a cost-effective and competitive manufacturing.Publication Metadata only A thermal-based defect classification method in textile fabrics with K-nearest neighbor algorithm(SAGE PUBLICATIONS INC, 2016) BULDU, ALİ; Yildiz, Kazim; Buldu, Ali; Demetgul, MustafaIn this study, fabric defects have been detected and classified from a video recording captured during the quality control process. Fabric quality control system prototype has been manufactured and a thermal camera was located on the quality control machine. The defective areas on the fabric surface were detected using the heat difference occurring between the defective and defect-free zones. Gray level co-occurrence matrix is used for feature extraction for defective images. The defective images are classified by k-nearest neighbor algorithm. The image processing stage consists of wavelet, threshold, and morphological operations. The defects have been classified with an average accuracy rate of 96%. In addition, the location of the defect has been identified and the defect type and location are recorded during the process via specially designed image processing interface. According to the experimental results, the proposed method works effectively.Publication Metadata only Development of a remote laboratory for an electronic circuit design and analysis course with increased accessibility by using speech recognition technology(WILEY, 2021) SARIKAŞ, ALİ; Yayla, Ayse; Korkmaz, Hayriye; Buldu, Ali; Sarikas, AliWhen the curricula of engineering undergraduate programs are examined, it can be seen that experimentation plays a very important role and the learning outcomes of the courses are mostly dependent on practical abilities. However, there may be a few who cannot use their hands permanently or temporarily among the students who are attending these courses. Therefore, the participation of disabled students in this part of the course has always been a problem. In this paper, a remote laboratory application that aims to increase the accessibility of electronic circuit design and analysis courses by using speech recognition technology is introduced. This laboratory is designed for hands-free operation and enables students to analyze the electronic circuits by speaking. Google Web Speech API was used for speech recognition and the user interface was designed using Adobe Flash Professional. The parameters are sent to the ASP.NET page by using ActionScript 2.0 programming language. The application developed by using C# programming language enables programming the experimental hardware that includes a signal generator, a Raspberry Pi 2 with a camera, an oscilloscope, and a new test card. In the Raspberry Pi 2, Python programming language was used to select the desired experiment from those present on the board and to control digitally programmable circuit components such as digital potentiometers or parameters such as the DC reference voltage level. When the student successfully completes the predefined experimental procedures, an automatically generated e-mail is sent to the instructor including the student's username, log-in time, the oscilloscope screenshots, and ideal experimental results.Publication Metadata only Detection of DDoS attacks with feed forward based deep neural network model(PERGAMON-ELSEVIER SCIENCE LTD, 2021) BULDU, ALİ; Cil, Abdullah Emir; Yildiz, Kazim; Buldu, AliAs a result of the increase in the services provided over the internet, it is seen that the network infrastructure is more exposed to cyber attacks. The most widely used of these attacks are Distributed Denial of Service (DDoS) attacks that easily disrupt services. The most important factor in the fight against DDoS attacks is the early detection and separation of network traffic. In this study, it is suggested to use the deep neural network (DNN) as a deep learning model that detects DDoS attacks on the sample of packets captured from network traffic. DNN model can work quickly and with high accuracy even in small samples because it contains feature extraction and classification processes in its structure and has layers that update itself as it is trained. As a result of the experiments carried out on the CICDDoS2019 dataset containing the current DDoS attack types created in 2019, it was observed that the attacks on network traffic were detected with 99.99% success and the attack types were classified with an accuracy rate of 94.57%. The high accuracy values obtained show that the deep learning model can be used effectively in combating DDoS attacks.Publication Metadata only A USB Kit for Digital I/O Applications in a Digital Electronics Lab Designed by Using PIC16C765 Microcontroller(WILEY, 2009) BULDU, ALİ; Buldu, Ali; Korkmaz, HayriyeIn this article, a USB Kit is designed by using Microchip's PIC16C765 microcontroller that has a low-speed USB serial interface engine. It is used to communicate with and/or through USB port for digital I/O applications in a Digital Electronics Lab In this education kit, two groups of keys (switches) and a group of LED are used to realize the experiments about logic gate applications included in Electronics and Computer Education Department's curriculum of Marmara University and also included in other faculties' curriculums related to the engineering science all around the world. In designed board, one of the key groups is 8-bit software-controlled by using simulator interface and the other is 8-bit user-controlled by using real switches existing on the board. (C) 2008 Wiley Periodicals, Inc. Comput Appl Eng Educ 17: 131-138. 2009: Published online in Wiley InterScience (www.interscience.wiley.com): DOI 10.1002/cae.20172