Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering

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Now showing 1 - 5 of 174
  • Publication
    Open Access
    A Design of an Integrated Cloud-Based Intrusion Detection System with Third Party Cloud Service
    (Walter de Gruyter GmbH, 2021) Elmasry, Wisam; AKBULUT, AKHAN; Zaim, Abdul Halim
    Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature. © 2021 Wisam Elmasry et al., published by De Gruyter 2021.
  • Publication
    Restricted
    Adaptation of Gamification as a Man-Machine Interface to Franchise Management System
    (Institute of Electrical and Electronics Engineers Inc., 2021, [Date of Conference: 21-23 October 2021]) ALTUNEL, YUSUF; GÜNAYDIN, BURAK; KALABALIKOĞLU, FURKAN
    Current man-machine interfaces are far from fitting human expectations in understanding and transmission of noteworthy signs and hints that are natural ingredients of human communication. There are certain indicators and interaction possibilities that can be passed between the two sides but as a result of complex behavior and dynamic changing conditions of environments they can be lost, or at least might require long and complex processing. Gamification is used to enhance the interaction possibilities providing the visual representation of environmental conditions and critical indicators, as well as providing the ability to send and receive requests using suitable techniques for human such as touches, and finger moves on Unity environment. Franchise Management System is selected as a case study and adapted to maintain the interactions between franchiser and franchisee. © 2021 IEEE.
  • Publication
    Restricted
    Deep Learning Based Methods for Processing Data in Telemarketing-Success Prediction
    (Institute of Electrical and Electronics Engineers Inc., 2021, [Date of Conference: 04-06 February 2021]) TÜRKMEN, EGEMEN
    In recent years, the importance of data has been increasing day by day. This has led companies to choose and use them actively, especially for reaching valuable information. Thanks to the interpretation of data, companies can save both time, labor, and costs for these operations in many application areas such as finance, security, e-commerce, data mining, etc. One critical area focuses on the use of finance, in which if the companies properly interpret and use this data, they can directly achieve more successful results in terms of their offering to customers with more accurate campaigns. In this paper, some deep learning methods (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Simple Recurrent Neural Network (SimpleRNN)) are used to predict the possibility of subscribing to deposit after the customer is called within the scope of the bank telemarketing campaign. Implemented models are tested with the used dataset and experimental results were compared and interpreted. To improve the obtained accuracy level different approaches are applied to the dataset. Because of the unbalanced structure of the used dataset, SMOTE approach was used to reach more accurate results. After the dataset is processed to be a balanced form, some deep learning methods are applied to it. Obtained results had compared with other proposals. Experimental results showed that the proposed algorithms gave a very acceptable prediction, and it is expected to be used in the finance sector. © 2021 IEEE.
  • Publication
    Open Access
    Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study
    (MDPI, 2021) Mishra, Alok; Shatnawi, Raed; Çatal, Çağatay; AKBULUT, AKHAN
    Several aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.
  • Publication
    Open Access
    Nonlocal Adaptive Direction-Guided Structure Tensor Total Variation for Image Recovery
    (Springer London Ltd., 2021) TÜREYEN, EZGİ DEMİRCAN; Kamasak, Mustafa E.
    A common strategy in variational image recovery is utilizing the nonlocal self-similarity property, when designing energy functionals. One such contribution is nonlocal structure tensor total variation (NLSTV), which lies at the core of this study. This paper is concerned with boosting the NLSTV regularization term through the use of directional priors. More specifically, NLSTV is leveraged so that, at each image point, it gains more sensitivity in the direction that is presumed to have the minimum local variation. The actual difficulty here is capturing this directional information from the corrupted image. In this regard, we propose a method that employs anisotropic Gaussian kernels to estimate directional features to be later used by our proposed model. The experiments validate that our entire two-stage framework achieves better results than the NLSTV model and two other competing local models, in terms of visual and quantitative evaluation.