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    Analysis Survey on Deepfake detection and Recognition with Convolutional Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ahmed, S.R.; Sonuc, E.; Ahmed, M.R.; Duru, A.D.
    Deep Learning (DL) is the most efficient technique to handle a wide range of challenging problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The development of DL has raised some privacy, justice, and national security issues. Deepfake is a DL-based application that has been very popular in recent years and is one of the reasons for these problems. Deepfake technology can create fake images and videos that are difficult for humans to recognize as real or not. Therefore, it needs to be proposed some automated methods for devices to detect and evaluate threats. In another word, digital and visual media must maintain their integrity. A set of rules used for Deepfake and some methods to detect the content created by Deepfake have been proposed in the literature. This paper summarizes what we have in the critical discussion about the problems, opportunities, and prospects of Deepfake technology. We aim for this work to be an alternative guide to getting knowledge of Deepfake detection methods. First, we cover Deepfake history and Deepfake techniques. Then, we present how a better and more robust Deepfake detection method can be designed to deal with fake content. © 2022 IEEE.
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    Classification of VPN Network Traffic Flow Using Time Related Features on Apache Spark
    (Institute of Electrical and Electronics Engineers Inc., 2020) Aswad, S.A.; Sonuc, E.
    This paper classifies the VPN network traffic flow using the time related features on the Apache Spark and artificial neural networks. Today's, internet traffic is encrypted using protocols like VPN/Non-VPN. This situation prevents the classic deep packet inspection approaches by analyzing packet payloads. For the implementation of this research, MATLAB 2019b would be forwarded in use as increasing demand for VPN networks has actuated the evolutionary technology. The proposed method will prevent unnecessary processing as well as flooding found in standard VPN network traffic classification. As the proposed system is trained on 80 of the dataset while 20% is kept for the testing and validation with 10-cross fold validation as well as 50 epochs of training. To the best of our knowledge, this is the first study that introduces and utilizes artificial neural networks and apache spark engine to implement the classification of VPN network traffic flow. The accuracy of the VPN classification using ANN and Apache Spark Engine is 96.76%. The accuracy of the Non-VPN classification using the proposed method is 92.56%. This study has shown that an approach using the CIC-Darknet2020 for packet-level encrypted traffic classification cannot incorporate packet header information, as it allows to directly map a packet to a specific application with high accuracy. Considering only non-VPN traffic, 96.76% of all packets in the dataset can be associated with an application. The remaining packets can still be classified with high probability by predicting based on the applications that use this flow. © 2020 IEEE.
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    Thyroid Disease Classification Using Machine Learning Algorithms
    (IOP Publishing Ltd, 2021) Salman, K.; Sonuc, E.
    With the vast amount of data and information difficult to deal with, especially in the health system, machine learning algorithms and data mining techniques have an important role in dealing with data. In our study, we used machine learning algorithms with thyroid disease. The goal of this study is to categorize thyroid disease into three categories: hyperthyroidism, hypothyroidism, and normal, so we worked on this study using data from Iraqi people, some of whom have an overactive thyroid gland and others who have hypothyroidism, so we used all of the algorithms. Support vector machines, random forest, decision tree, naïve bayes, logistic regression, k-nearest neighbors, multi-layer perceptron (MLP), linear discriminant analysis. To classification of thyroid disease. © Published under licence by IOP Publishing Ltd.