45 results
Search Results
Now showing 1 - 10 of 45
Article Citation - WoS: 38Citation - Scopus: 47Understanding Key Skills for Information Security Managers(Elsevier Sci Ltd, 2018) Haqaf, Husam; Koyuncu, MuratInformation security management is a necessity for all institutions and enterprises that regard company information as valuable assets. Developing, auditing and managing information security depends upon professional expertise in order to achieve the desired information security governance. This research seeks the key skills required for the position of information security management as well as the methods to develop these skills through professional training programs. The study adopts the Delphi method which requires building a list of items through a literature survey and involves experts with certain expertise to modify the list until a consensus on less than 20% of the items is reached. Through completing three rounds of the Delphi technique - data collection, relevance voting and ranking sixteen skills are shortlisted as the key skills. In the final list, the majority belong to core information security skills, and the top two skills belong to project/process management skills and risk management skills, indicating the importance of these skills for the information security manager role. In addition, a series of related professional training programs and certifications are surveyed, the outcome of which highlights a number of most comprehensive and appropriate programs to develop these determined skills.Article Citation - WoS: 7Citation - Scopus: 10A FUZZY CONCEPTUAL MODEL FOR MULTIMEDIA DATA WITH A TEXT-BASED AUTOMATIC ANNOTATION SCHEME(World Scientific Publ Co Pte Ltd, 2009) Kucuk, Dilek; Burcuozgur, N.; Yazici, Adnan; Koyuncu, MuratThe size of multimedia data is increasing fast due to the abundance of multimedia applications. Modeling the semantics of the data effectively is crucial for proper management of it. In this paper, we present a fuzzy conceptual data model for multimedia data which is also generic in the sense that it can be adapted to all multimedia domains. The model takes an object-oriented approach and it handles fuzziness at different representation levels where fuzziness is inherent in multimedia applications and should be properly modeled. The proposed model also has the nice feature of representing the structural hierarchy of multimedia data as well as the spatial and temporal relations of the data. The model is applied to the news video domain and implemented as a fuzzy multimedia database system where it turns out to be effective in representing the domain and thereby provides an evidence for the general applicability of the model. The model is accompanied by an automatic multimedia annotation scheme which makes use of information extraction techniques on the corresponding multimedia texts.Article A Comprehensive Assessment Plan for Accreditation in Engineering Education: A Case Study in Turkey(International Journal of Engineering Education, 2015) Turhan, Çiğdem; Şengül, Gökhan; Koyuncu, MuratThis paper describes the procedure followed by Computer Engineering and Software Engineering programs at Atilim University, Ankara, Turkey, which led to the granting of five years of accreditation by MUDEK, the local accreditation body authorized by The European Network for Accreditation of Engineering Education (ENAEE) to award the EUR ACE label, and a full member signatory ofWashington Accord of International Engineering Alliance (IEA). It explains the organizational structure established for preparation, determination and measurement of the educational objectives, program outcomes, course outcomes, and the continuous improvement cycle carried out during the preparation period. The aim of the paper is to share methods and experiences which may be beneficial for the other programs that are intended for accreditation.Article Citation - WoS: 3Citation - Scopus: 5Particle Swarm Optimization of the Spectral and Energy Efficiency of an Scma-Based Heterogeneous Cellular Network(Wiley, 2022) Noma-Osaghae, Etinosa; Misra, Sanjay; Ahuja, Ravin; Koyuncu, MuratBackground The effect of stochastic small base station (SBS) deployment on the energy efficiency (EE) and spectral efficiency (SE) of sparse code multiple access (SCMA)-based heterogeneous cellular networks (HCNs) is still mostly unknown. Aim This research study seeks to provide insight into the interaction between SE and EE in SBS sleep-mode enabled SCMA-based HCNs. Methodology A model that characterizes the energy-spectral-efficiency (ESE) of a two-tier SBS sleep-mode enabled SCMA-based HCN was derived. A multiobjective optimization problem was formulated to maximize the SE and EE of the SCMA-based HCN simultaneously. The multiobjective optimization problem was solved using a proposed weighted sum modified particle swarm optimization algorithm (PSO). A comparison was made between the performance of the proposed weighted sum modified PSO algorithm and the genetic algorithm (GA) and the case where the SCMA-based HCN is unoptimized. Results The Pareto-optimal front generated showed a simultaneous maximization of the SE and EE of the SCMA-based HCN at high traffic levels and a convex front that allows network operators to select the SE-EE tradeoff at low traffic levels flexibly. The proposed PSO algorithm offers a higher SBS density, and a higher SBS transmit power at high traffic levels than at low traffic levels. The unoptimized SCMA-based HCN achieves an 80% lower SE and a 51% lower EE than the proposed PSO optimized SCMA-based HCN. The optimum SE and EE achieved by the SCMA-based HCN using the proposed PSO algorithm or the GA are comparable, but the proposed PSO uses a 51.85% lower SBS density and a 35.96% lower SBS transmit power to achieve the optimal SE and EE at moderate traffic levels. Conclusion In sleep-mode enabled SCMA-based HCNs, network engineers have to decide the balance of SBS density and SBS transmit power that helps achieve the desired SE and EE.Article Citation - WoS: 33Citation - Scopus: 41Visual and Auditory Data Fusion for Energy-Efficient and Improved Object Recognition in Wireless Multimedia Sensor Networks(Ieee-inst Electrical Electronics Engineers inc, 2019) Koyuncu, Murat; Yazici, Adnan; Civelek, Muhsin; Cosar, Ahmet; Sert, MustafaAutomatic threat classification without human intervention is a popular research topic in wireless multimedia sensor networks (WMSNs) especially within the context of surveillance applications. This paper explores the effect of fusing audio-visual multimedia and scalar data collected by the sensor nodes in a WMSN for the purpose of energy-efficient and accurate object detection and classification. In order to do that, we implemented a wireless multimedia sensor node with video and audio capturing and processing capabilities in addition to traditional/ordinary scalar sensors. The multimedia sensors are kept in sleep mode in order to save energy until they are activated by the scalar sensors which are always active. The object recognition results obtained from video and audio applications are fused to increase the object recognition performance of the sensor node. Final results are forwarded to the sink in text format, and this greatly reduces the size of data transmitted in network. Performance test results of the implemented prototype system show that the fusing audio data with visual data improves automatic object recognition capability of a sensor node significantly. Since auditory data requires less processing power compared to visual data, the overhead of processing the auditory data is not high, and it helps to extend network lifetime of WMSNs.Article A Model Proposal To Optimize Bandwidth Usage in Multi-Access Wireless Networks(Univ Osijek, Tech Fac, 2012) Koyuncu, Murat; Gercek, Mehmet Kazim; Information Systems EngineeringDistribution of load among multiple access points, and therefore, optimizing the total throughput is one of the problems of wireless local area networks when there is more than one access point available in the network. It is not easy to balance the load when wireless hosts associate with one access point by using only the Received Signal Strength Indicator (RSSI). In this study, a new model which increases total throughput in a wireless local area network is proposed. The proposed model is mainly based on the prediction of the loads of all the available access points checking both their wireless and Ethernet interfaces and the association to the least loaded one. It is a host-based model and does not require any changes on the network infrastructure. The tests performed on the real wireless local area networks prove the applicability of the proposed model.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, RoohallahSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.Article Citation - Scopus: 1Optimizing the Stochastic Deployment of Small Base Stations in an Interleave Division Multiple Access-Based Heterogeneous Cellular Networks(Wiley, 2022) Noma-Osaghae, Etinosa; Misra, Sanjay; Koyuncu, MuratThe use of small base stations (SBSs) to improve the throughput of cellular networks gave rise to the advent of heterogeneous cellular networks (HCNs). Still, the interleave division multiple access (IDMA) performance in sleep mode active HCNs has not been studied in the existing literature. This research examines the 24-h throughput, spectral efficiency (SE), and energy efficiency (EE) of an IDMA-based HCN and compares the result with orthogonal frequency division multiple access (OFDMA). An energy-spectral-efficiency (ESE) model of a two-tier HCN was developed. A weighted sum modified particle swarm optimization (PSO) algorithm simultaneously maximized the SE and EE of the IDMA-based HCN. The result obtained showed that the IDMA performs at least 68% better than the OFDMA on the throughput metric. The result also showed that the particle swarm optimization algorithm produced the Pareto optimal front at moderate traffic levels for all varied network parameters of SINR threshold, SBS density, and sleep mode technique. The IDMA-based HCN can improve the throughput, SE, and EE via sleep mode techniques. Still, the combination of network parameters that simultaneously maximize the SE and EE is interference limited. In sleep mode, the performance of the HCN is better if the SBSs can adapt to spatial and temporal variations in network traffic.Article Citation - WoS: 30Citation - Scopus: 48A Fusion-Based Framework for Wireless Multimedia Sensor Networks in Surveillance Applications(Ieee-inst Electrical Electronics Engineers inc, 2019) Yazici, Adnan; Koyuncu, Murat; Sert, Seyyit Alper; Yilmaz, TurgayMultimedia sensors enable monitoring applications to obtain more accurate and detailed information. However, the development of efficient and lightweight solutions for managing data traffic over wireless multimedia sensor networks (WMSNs) has become vital because of the excessive volume of data produced by multimedia sensors. As part of this motivation, this paper proposes a fusion-based WMSN framework that reduces the amount of data to be transmitted over the network by intra-node processing. This framework explores three main issues: 1) the design of a wireless multimedia sensor (WMS) node to detect objects using machine learning techniques; 2) a method for increasing the accuracy while reducing the amount of information transmitted by the WMS nodes to the base station, and; 3) a new cluster-based routing algorithm for the WMSNs that consumes less power than the currently used algorithms. In this context, a WMS node is designed and implemented using commercially available components. In order to reduce the amount of information to be transmitted to the base station and thereby extend the lifetime of a WMSN, a method for detecting and classifying objects on three different layers has been developed. A new energy-efficient cluster-based routing algorithm is developed to transfer the collected information/data to the sink. The proposed framework and the cluster-based routing algorithm are applied to our WMS nodes and tested experimentally. The results of the experiments clearly demonstrate the feasibility of the proposed WMSN architecture in the real-world surveillance applications.Master Thesis Kullanıcı Odaklı Yaklaşımlarla Mobil Güvenliğin Değerlendirilmesi ve Geliştirilmesi(2025) Alburkı, Hussaın Taha Hussaın; Koyuncu, MuratMobil uygulamalar günümüzde bireylerin finansal, tıbbi ve kişisel etkileşimlerinin giderek artan bir kısmını yönetmektedir. Ancak güvenlik araştırmaları hâlâ büyük ölçüde kod düzeyindeki açıklar üzerine yoğunlaşmakta ve sıradan kullanıcıların bu koruma mekanizmalarıyla nasıl etkileşime geçtiğini göz ardı etmektedir. Bu boşluğu kapatmak amacıyla, OWASP Mobile Top 10, MITRE ATT&CK ve güncel akademik literatürde tanımlanan teknik tehditleri sade bir dille sekiz uygulanabilir adıma dönüştüren bir kontrol listesi geliştirilmiştir. Bu adımlar; aşırı izin verme, güvensiz ağ kullanımı ve zayıf kimlik doğrulama gibi temel güvenlik açıklarını hedef alır ve teknik olmayan terimlerle ifade edilerek geniş bir kullanıcı kitlesi için erişilebilir hale getirilmiştir. Çalışma üç aşamalı bir yöntem izlemiştir: zafiyet analizi yoluyla kontrol listesi geliştirme, kullanıcı davranışlarını ve liste kullanımını değerlendiren bir anket uygulaması ve katılımcı geri bildirimlerine dayalı rehber ilke oluşturma. Geliştirilen kontrol listesi, yaş, platform ve siber güvenlik deneyimi bakımından çeşitlilik gösteren 42 Android ve iOS kullanıcısıyla test edilmiştir. Katılımcıların %83'ü kontrol listesindeki adımların çoğunu tamamlamış, %70'ten fazlası ise mobil riskler konusunda farkındalıklarının arttığını bildirmiştir. Bu bulgulara dayanarak teknik standartlarla kullanıcı pratiği arasındaki boşluğu doldurmayı amaçlayan on ilke geliştirilmiştir. Her ilke, belirli kullanıcı eylemlerini bilinen zafiyetlerle eşleştirmekte ve OWASP ile MITRE gibi güvenlik çerçeveleriyle uyum göstermektedir. Sonuçlar, teknik temelli davranışsal içgörülerle geliştirilen kullanıcı odaklı araçların farkındalığı artırma ve daha güvenli alışkanlıklar kazandırma potansiyeline sahip olduğunu vurgulamaktadır.
