TR
FIRATUNIVERSITY
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ABOUT

DETAILS
GPS: 38.680955 - 39.195835
fatih@ertam.com
+90 424 2370000
F.U Assistant Professor F.U Network Administrator F.U ULAKBIM Administrator CISCO Network Instructer F.U Network Administrator
Welcome to my website Academical personal

BIOGRAPY

ABOUT ME

I am Assistant Professor in the department of Digital Forensic Engineering in Firat University. I completed my Ph.D. in Software Engineering. About 17 years (December 2000 - December 2017) I worked as a computer lecturer in the Department of Informatics at Firat University.

I am Assistant Technical Manager at Distance Education Center. I'm vice-dean of The Faculty of Technology. At different times I worked for about 8 years as Vice President of Informatics Department. I have been managing the network systems for many years at Firat University Computer Center.

I work as the founder and general manager of NETKORU Company, which operates in the field of internet security software and network systems in Firat Technopolis.

INTERESTS

PROJECT

Machine learning, artificial intelligent, deep learning, intelligent classification

Network devices management, network security, network digital forensics, information security

Social media data mining, behavior analysis

LESSONS - 2018-2019 Spring Semester

Office hours

ABM427 Machine Learning
ABM210 Network and System Security

ABM539 Security in Computer Networks
ABM536 Performance Analysis in Computer Networks

Student office hours Friday 14:00-16:00. I do not have any classes between these hours. you can come to the room and ask your questions.


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CAREER

  • JOB EXPERIENCE
  • NOW
    2018
    Elazığ

    Assistant Professor

    FIRAT UNIVERSITY DIGITAL FORENSICS ENGINEERING

    Fırat University Technology Faculty, Digital Forensics Eng.
  • 2018
    2016
    Elazığ

    Lecturer, PhD.

    FIRAT UNIVERSITY INFORMATIC DEPARTMENT

    Fırat University Informatics Department Computer Instructor.
  • 2016
    2000
    Elazığ

    LECTURER

    FIRAT UNIVERSITY INFORMATIC DEPARTMENT

    Fırat University Informatics Department Computer Instructor.
  • NOW
    2017
    Elazığ

    GENERAL MANAGER

    NETKORU IT

    Netkoru Bilişim Yazılım Eğitim Danışmanlık San. Tic. Ltd. Şti. General Manager.
  • 2000
    2000
    Malatya

    TEACHER

    MINISTRY OF EDUCATION

    Ministry of National Education, Doğanşehir Multi-Program High School, Computer Teacher.
  • EDUCATION
  • 2012
    2016
    Elazığ

    SOFTWARE ENGINEERING - PhD DEGREE

    FIRAT UNIVERSITY

    Fırat University, Institute of Science and Technology, Department of Software Engineering. Thesis Name: Intelligents systems with classification of traffic information in corporate computer networks
  • 2003
    2005
    Elazığ

    ELECTRONIC COMPUTER EDUCATION - MASTER DEGREE

    FIRAT UNIVERSITY

    Fırat University, Institute of Science and Technology, Electronic and Computer Education. Thesis Name: Remote control of a computer via internet and computer networks
  • 1996
    2000
    Elazığ

    COMPUTER TEACHING - UNDERGRADUATE EDUCATION

    FIRAT UNIVERSITY

    Fırat University Technical Education Faculty, Computer Teaching
  • ADMINISTRATIVE POSITIONS
  • NOW
    2018
    Elazığ

    VICE-DEAN

    FIRAT UNIVERSITY TECHNOLOGY FACULTY

    Fırat University, Technology Faculty, vice-dean
  • 2004
    2006
    Elazığ

    VICE PRESIDENT

    FIRAT UNIVERSITY INFORMATIC DEPARTMENT

    Fırat University, Department of Informatics, vice president
  • 2006
    2007
    Elazığ

    VICE PRESIDENT

    FIRAT UNIVERSITY INFORMATIC DEPARTMENT

    Fırat University, Department of Informatics, vice president
  • 2018
    2012
    Elazığ

    VICE PRESIDENT

    FIRAT UNIVERSITY INFORMATIC DEPARTMENT

    Fırat University, Department of Informatics, vice president
  • 2010
    2012
    Elazığ

    ASSISTANT DIRECTOR

    FIRAT UNIVERSITY, DISTANCE EDUCATION CENTER

    Fırat University Distance Education Center, Assistant Manager
  • NOW
    2012
    Elazığ

    ASSISTANT DIRECTOR

    FIRAT UNIVERSITY, DISTANCE EDUCATION CENTER

    Fırat University Distance Education Center, Assistant Manager
  • OTHER
  • NOW
    2012
    Elazığ

    NETWORK ADMIN

    TUBİTAK ULAKBİM

    Tubitak Ulakbim, Fırat University Network Manager
  • 2003
    2004
    Elazığ

    BOARD MEMBER

    FIRAT HABER NEWSPAPER

    Fırat University, Fırat Haber Newspaper Board Member
  • 2002
    2003
    Elazığ

    WEB MANAGER

    FIRAT UNIVERSITY

    Fırat University Website manager
  • 2012
    2013
    Elazığ

    SOFTWARE ENGINEERING

    FIRAT UNIVERSITY

    Firat University Software Engineering postgraduate student representative
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PUBLICATIONS

SELECTED PUBLICATION LIST
ALL
SORTING BY DATE
1 OCT 2017

Data classification with deep learning using Tensorflow

Computer Science and Engineering (UBMK), Antalya

Deep learning is a subfield of machine learning which uses artificial neural networks that is inspired by the structure and function of the human brain. Despite being a very new approach, it has become very popular recently. Deep learning has achieved much higher success in many applications where machine learning has been successful at certain rates.
Conference International Fatih Ertam, Aydin, G.
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Data classification with deep learning using Tensorflow

Fatih Ertam, Aydin, G. Conference International

Deep learning is a subfield of machine learning which uses artificial neural networks that is inspired by the structure and function of the human brain. Despite being a very new approach, it has become very popular recently. Deep learning has achieved much higher success in many applications where machine learning has been successful at certain rates. In particular It is preferred in the classification of big data sets because it can provide fast and efficient results.

In this study, we used Tensorflow, one of the most popular deep learning libraries to classify MNIST dataset, which is frequently used in data analysis studies. Using Tensorflow, which is an open source artificial intelligence library developed by Google, we have studied and compared the effects of multiple activation functions on classification results. The functions used are Rectified Linear Unit (ReLu), Hyperbolic Tangent (tanH), Exponential Linear Unit (eLu), sigmoid, softplus and softsign. In this Study, Convolutional Neural Network (CNN) and SoftMax classifier are used as deep learning artificial neural network. The results show that the most accurate classification rate is obtained using the ReLu activation function.

31 JAN 2017

A new approach for internet traffic classification: GA-WK-ELM

Measurement

The classification of internet traffic is one of the very popular topics of the present- day. In particular, the classification studies performed along with the use of machine learning (ML) approaches is increasing a little more with each passing day.
Journal SCI-Exp Fatih Ertam, Avci, E.
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A new approach for internet traffic classification: GA-WK-ELM

Fatih Ertam, Avci, E. Journal

The classification of internet traffic is one of the very popular topics of the present-day. In particular, the classification studies performed along with the use of machine learning (ML) approaches is increasing a little more with each passing day. In this study, Extreme Learning Machine (ELM) methods were used for the classification of Internet traffic. Kernel Based Extreme Learning Machine (KELM) approach, one of the ELM approaches, was applied to the data.

In particular, Genetic Algorithm (GA) based software (GA-WK-ELM) was developed for the selection of the parameters which were used in the (WK-ELM) algorithm in which Wavelet function was used. It was seen that an accuracy rate of over 95% was achieved along with the application developed with GA. The average truth value metric was used in order to compare the performance of the classification performed. In addition, Receiver Operating Characteristic (ROC) curves were also generated.

1 JUN 2005

Remote control of a computer via internet and computer networks

MASTER THESIS

In this thesis, a computer is controlled, turned on, turned off or rebooted, by a remote computer via internet. This is achieved in two stages as software and hardware.
Thesis Master Fatih Ertam, Advisor: Güldemir, H.
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REMOTE CONTROL OF A COMPUTER VIA INTERNET AND COMPUTER NETWORKS

Fatih Ertam Advisor: Güldemir, H. Thesis

In this thesis, a computer is controlled, turned on, turned off or rebooted, by a remote computer via internet. This is achieved in two stages as software and hardware.

Software is developed by using web based programming languages ASP and PHP. The required command is sent to the serial port of the remote host computer by using one of the developed software. A Hardware is designed and connected to the serial port of the host computer. This hardware consists of a microcontroller and receives and process the data received. When the data is received and processed one of the commands turn on, turn off or reboot is sent to the pins of the power system on the main board of the target computer.

1 JAN 2003

C İle Programlamaya Giriş

Section Name:Deyimler

Publication:Fırat Üniversitesi Basımevi, Editor:Balık, H., Basım sayısı:1, Page Number:200, ISBN:975-394-029-7, Section pages:51 -70
Book Chapter Fatih Ertam
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C İle Programlamaya Giriş

Fatih Ertam Book Chapter

Publication:Fırat Üniversitesi Basımevi, Editor:Balık, H., Basım sayısı:1, Page Number:200, ISBN:975-394-029-7, Section pages:51 -70

1 JAN 2005

Temel Bilgi Teknolojisi Kullanımı

Section Name:İnternet ve İletişim

Editor:Kürüm, H., Basım sayısı:1, Page Number:300, ISBN:975-6305-09-6, Section pages:173 -189
Book Chapter Nobel Yayınevi Fatih Ertam
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Temel Bilgi Teknolojisi Kullanımı

Fatih Ertam Book Chapter Nobel Yayınevi

Editor:Kürüm, H., Basım sayısı:1, Page Number:300, ISBN:975-6305-09-6, Section pages:173 -189

24 SEP 2017

An Effective Classification Method for Facebook Data

International Journal of Applied Mathematics, Electronics and Computers

Today, the use of the internet has become very common. One of the most important reasons for its widespread use is social media tools. Especially Facebook has a very important place in social media tools.
Journal Fatih Ertam
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An Effective Classification Method for Facebook Data

Fatih Ertam Journal

Today, the use of the internet has become very common. One of the most important reasons for its widespread use is social media tools. Especially Facebook has a very important place in social media tools.

For this study, classification was done by using Facebook data. Classifications made by artificial learning algorithms on a previously used data set are compared with accuracy values and learning times. For this purpose, support vector machines (SVM), extreme learning machines (ELM) and K nearest neighbor (kNN) approaches are compared. For the study, SVM and ELM algorithms were observed using different activation functions. For the study with KNN, different K values were tested with different distance metric calculation methods. In the classification approach with ELM, it was observed that higher accuracy values were reached in a shorter time. In addition, Receiver Operating Characteristic (ROC) curves are plotted for the classification in which the best values are obtained for each algorithm

30 JAN 2016

Uç Öğrenme Makineleri Kullanılarak Internet Trafik Bilgisinin Sınıflandırılması

Akademik Bilişim 2016, Aydın

Son zamanlarda nesnelerin interneti (internet of things, IoT) kavramı ile internetin kullanımının çok yüksek düzeylere ulaşması sebebiyle internet ile beraber sunulan servis kalitesinin artırılması, ağın verimli kullanılması ve farklı hizmet paketlerinin oluşturulabilmesi gibi konuların önemi daha da artmıştır.
Conference Fatih Ertam, Avci, E.
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Uç Öğrenme Makineleri Kullanılarak Internet Trafik Bilgisinin Sınıflandırılması

Fatih Ertam, Avci, E. Conference

Son zamanlarda nesnelerin interneti (internet of things, IoT) kavramı ile internetinkullanımının çok yüksek düzeylere ulaşması sebebiyle internet ile beraber sunulan serviskalitesinin artırılması, ağın verimli kullanılması ve farklı hizmet paketlerinin oluşturulabilmesigibi konuların önemi daha da artmıştır. İnternet üzerinden akan trafik verisinin sınıflandırılması,özellikle büyük ağlarda güvenliğin sağlanması, trafik yönetiminin etkin bir şekilde yapılabilmesiiçin oldukça önemli bir hale gelmiştir. Internet trafiğini hızlı bir şekilde artması ve kullanılanuygulamaların çeşitliliği ağın kontrol edilebilmesi için ağ yöneticileri tarafından bu bilgininbilinmesi neredeyse bir zorunluluk olmaya başlamıştır.

Sınıflandırma için yaygın olarak port, yük ve istatistik bilgileri kullanılmıştır. Port ve yük tabanlı yaklaşımlar ile yapılabileceksınıflandırma seçenekleri sınırlı olduğu için özellikle denetimli makine öğrenme (ML)algoritmaları internet trafik sınıflandırılmasında sıklıkla uygulanmaya başlamıştır. Destekvektör makinesi (DVM) ve yapay sinir ağları (YSA) tabanlı sınıflandırıcılar önceki çalışmalardaoldukça fazla kullanılmıştır. Yapılan çalışmada klasik sınıflandırıcı yöntemler yerine Uçöğrenme makinesi (UÖM) algoritması kullanılmıştır. UÖM ile yapılan sınıflandırmabaşarımının daha yüksek olduğu görülmüştür.

1 JUN 2016

Intelligents systems with classification of traffic information in corporate computer networks

PhD Thesis

The classification of data on the internet in order to make internet use more efficient has an important place especially for network administrators managing corporate networks. Studies for the classification of internet traffic have increased recently. By these studies, it is aimed to increase the quality of service on the network, use the network efficiently, create the service packages and offer them to the users.
Thesis PhD Fatih Ertam, Advisor: Avci, E.
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Intelligents systems with classification of traffic information in corporate computer networks

Fatih Ertam Advisor: Avci, E. Thesis PhD

The classification of data on the internet in order to make internet use more efficient has an important place especially for network administrators managing corporate networks. Studies for the classification of internet traffic have increased recently. By these studies, it is aimed to increase the quality of service on the network, use the network efficiently, create the service packages and offer them to the users.

The first classification method used for the classification of the internet traffic was the classification for the use of port numbers. This classification method has already lost its validity although it was an effective and quick method of classification for the first usage times of the internet. Another classification method used for the classification of network traffic is called as payload-based classification or deep packet inspection. This approach is based on the principle of classification by identifying signatures on packets flowing on the network. Another method of classification of the internet traffic which is commonly used in our day and has been also selected for this study is the extreme learning machine based approaches. It is based on classifying by the use of more statistical methods and algorithms collecting the flow information on the network. For the classification of the internet traffic, extreme learning machines (ELM), which were hardly ever used in previous studies, were used. In order to compare the performance of ELM, support vector machines (SVM), Naive Bayes (NB) and artificial neural networks (ANN) from learning machine algorithms used before were also compared by applying to data set. It was observed that a faster and higher level performance was achieved in the classification made with ELM algorithms compared to other learning machine algorithms. Classification was made by applying both classical and kernel based ELM (KELM) approaches to data. The receiver operating characteristic (ROC) curves were created for each class of the classifiers. In particular, wavelet function which uses the KELM algorithm used parameters for the selection of a good degree of genetic algorithm (GA) based software (GA-WF-KELM) was developed.

9 MAY 2017

An Intelligent Classification Approach for Social Media Data

International Conference on Advanced Technology & Sciences ICAT’17, İstanbul

Social media has a great place in today's internet usage. In particular, it is important for companies that want to advertise on social media to invest in which area. Facebook has the highest usage rate among social media tools. In this study, artificial intelligence techniques were used to classify a data set generated with Facebook data.
Conference International Fatih Ertam
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An Intelligent Classification Approach for Social Media Data

Fatih Ertam Conference International

Social media has a great place in today's internet usage. In particular, it is important for companies that want to advertise on social media to invest in which area. Facebook has the highest usage rate among social media tools. In this study, artificial intelligence techniques were used to classify a data set generated with Facebook data. In order to achieve this purpose, support vector machines (SVM) and extreme learning machines (ELM) are compared. The results of the studies were evaluated with accuracy values and ROC curves.

In addition, the processing speeds of the classifiers were also checked and compared. In this study, an intelligent classifier that could be preferred for social media data was tried to be offered. Different activation functions for both classifiers were selected and compared among themselves.

23 JAN 2013

Importance of Network Devices and Their Secure Configurations at Digital Forensics

ISDFS 2013, Elazığ

Network forensics is the capture, recording, and analysis of network events in order to discover the source of security attacks or other problem incidents.
Conference Fatih Ertam, Tuncer, T., Avci, E.
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Importance of Network Devices and Their Secure Configurations at Digital Forensics

Fatih Ertam, Tuncer, T., Avci, E. Conferences

Network forensics is the capture, recording, and analysis of network events in order to discover the source of security attacks or other problem incidents. Network forensics is a sub-branch of digital forensics. Network device forensics is also a sub-branch of network digital forensics.

Forensic IT applications and examination of suspicious users' personal computers to be copied to all of the data alone is not sufficient for the determination of guilt or innocence. Users before the event and during the event is logged on the network traffic to be carried out is important. These records are necessary to preserve the security measures on the network devices, the devices required by the user on the network can not use, the internet user's IP address and MAC address of the output to be significant, the user attempts to disrupt the functioning of the network is very important in minimizing. Users, network devices must not interfere with the records. In this study, as well as to facilitate network forensics investigations on network devices to improve network security configuration settings to be discussed.

16 SEP 2017

Intrusion detection in computer networks via machine learning algorithms

Artificial Intelligence and Data Processing Symposium (IDAP), Malatya

With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount.
Conference International Fatih Ertam, Kılınçer, İ.F., Yaman, O.
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Intrusion detection in computer networks via machine learning algorithms

Fatih ErtamKılınçer, İ.F., Yaman, O. Conference International

With the internet of objects, the number of devices with internet connection is increasing day by day. This leads to a very high amount of data circulating on the internet. It is one of the most common problems that can be distinguished from normal and abnormal traffic by analyzing in high data amount. In this study, an analysis was carried out by using machine learning approaches to determine whether the data received on the internet is normal or abnormal data.

In order to achieve this goal, the KDD Cup 99 data set which is frequently used in literature studies is classified by Naive Bayes (NB), bayes NET (bN), Random Forest (RF), Multilayer Perception (MLP) and Sequential Minimal Optimization (SMO) algorithms. Classifiers are also compared with false rate, precision, recall, and F measure metrics along with accuracy rate values. Classification times of classifiers are also given by comparison.

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RESEARCH

PROJECT TEAM

COMPUTER ENGINEER

SOFTWARE TEAM LEADER

Manages software preparation part of projects. Performs testing and analysis of software, manages software team

ELECTRONICS ENGINEER

NETWORK TEAM LEADER

Designs hardware devices on which software runs, leads network team

RESEARCH PROJECTS

SECURE INTERNET AREA

TUBITAK 1512 INDIVIDUAL ENTREPRENEURSHIP PROJECT / EXECUTIVE

It is intended to develop a network device that will create a "Secure Internet Zone" for home and small businesses. Product AR-GE work is continuing.

After the commercial prototype serial production will be passed.

FIRAT UNIVERSITY WIRELESS NETWORK INFRASTRUCTURE REINFORCEMENT PROJECT

FIRAT UNIVERSITY SCIENTIFIC RESEARCH PROJECT / RESEARCHER

In order to provide wireless internet service in all indoor and outdoor areas within the campus with the WI-FI project of Fırat University, outdoor and indoor Access Point devices were designed and installed together with active active control devices.

By the year of 2017, wireless range has been expanded. 200+ devices are active.

BUSINESS SOLUTION PROFESSIONAL PROJECT

EUROPEAN UNION PROJECT / TRAINER

TURKEY TR 0405.02/LDI/356

Elazig - Bingol has been provided with computer training for participants as part of the project of the chamber of accountants financial accountants.

ETSO MEMBERS PROJECT TO PROVIDE EFFICIENT USE OF ICT TECHNOLOGIES

EUROPEAN UNION PROJECT / TRAINER

TURKEY TR 0405.02/LDI/141

Elazig Chamber of Commerce and Industry hosted computer training for participant

ANALYSIS OF INTERNET DATA WITH DEEP LEARNING

FIRAT UNIVERSITY SCIENTIFIC RESEARCH PROJECT / EXECUTIVE

Analysis and classification of data received via internet will be done by using deep learning approaches.

The project is still ongoing.

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CERTIFICATES

  • INSTRUCTOR
  • 2017

    Palo Alto Networks Academy Instructor

    Palo Alto Networks Academy Instructor Certificate Number:12242014-001

  • 2006

    CCNA 1 - Networking Basics

    Turkish Academic Network and Information Center, Academy ID: 4692147

  • 2006

    CCNA 2 - Router and Routing Basics

    Turkish Academic Network and Information Center, Academy ID: 4692147

  • 2004

    ECDL Test Responsible

    European Computer Driving Licence

  • TRAINING
  • 2016

    Extreme Switching

    Extreme Networks

  • 2015

    Innovation Management Academy

    Turkiye Ihracatcilar Meclisi - Ege Ihracatci Birlikleri

  • 2015

    Introduction to Cybersecurity

    Ag Guvenligi Dernegi

  • 2009

    Enterasys Switching and Routing

    Enterasys Networks

  • 2006

    Oracle Application Server Database Applications

    Faruk Cubukcu Egitim Merkezi

  • 2006

    Application Development with PL / SQL

    Faruk Cubukcu Egitim Merkezi

  • 2006

    Network Security Concepts and FORTINET Security Systems

    RZK Muhendislik ve Bilgisayar Sistemleri

  • 2001

    Updt. Supp. Skills from MS Win NT Serv. to Win2000

    Microsoft Technical Education Center

  • 2005

    Oracle9i Database Administration Fundamentals I

    Oracle University

  • 2005

    Oracle9i: Introduction to SQL

    Oracle University

  • 2006

    Open Source Code Operating Systems

    Linux Kullanicilari Dernegi - Pamukkale University

  • PARTICIPATION
  • 2016

    Preparation and Implementation of Tubitak Research Projects for Engineering and Technological Sciences

    Firat University

  • 2017

    Tubitak Project Preparation and Execution Training for Industry Transfer of Research Projects

    Firat University

  • 2013

    7. ULAKNET Workshop and Training / ULAKNETCE 2013

    Ataturk University

  • 2015

    9. ULAKNET Workshop and Training / ULAKNETCE 2015

    Dokuz Eylul University

  • 2016

    10. ULAKNET Workshop and Training / ULAKNETCE 2016

    Mugla Sitki Kocman University

  • 2017

    11. ULAKNET Workshop and Training / ULAKNETCE 2017

    Akdeniz University

  • 2017

    12. ULAKNET Workshop and Training / ULAKNETCE 2018

    TUBITAK

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NETKORU IT

ABOUT THE COMPANY
Netkoru IT was established in 2017 by the Scientific and Technological Research Council of Turkey (TUBITAK) Support. We are working on the development of products that will provide "Secure Internet Space" service on Fırat Teknokent R & D.
www.netkoru.com.tr || www.netkoru.net || www.netkoru.com
info@netkoru.net || fatih.ertam@netkoru.net
+90 850 532 1323
Fırat Teknokent AR-GE Ek Bina 63 / Z03 ELAZIĞ, TURKEY
SECURE INTERNET
Our primary goal is to do R & D work for the development of a network device that will create a "Secure Internet Zone" for home and small businesses..
LEVEL : R & DEXPERIENCE : 2 YEARS
PhpPythonIPS/IDS
CORPORATE NETWORK AND SYSTEM SOLUTIONS
Since 2000, all the needs of your network from the end to the end have been met with the help of educated personnel who have set up and managed large networks in the sector
LEVEL : EXPERTEXPERIENCE : 17 YEARS
CiscoLayer1-Layer7VirtualizationCabling
EDUCATION
Remote and cross-site trainings are given in the field of network and systems management with our experienced staff who are active in the field with CISCO trainer certifications.
LEVEL : ADVANCEDEXPERIENCE : 10 YEARS
CCNACCNPDistance Learning
CONSULTANCY
It is contributed to the improvement of your projects with the academic consultant who has long experience in project development and management.
LEVEL : INTERMEDIATEEXPERIENCE : 2 YEARS
TubitakKosgebTeknokent
SOFTWARE DEVELOPMENT
Our staff who have completed their Ph.D. in Software Engineering field, plan your entire software development process.
LEVEL : ADVANCEDEXPERIENCE : 5 YEARS
Open source codeDatabaseProgramming
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WORKS

BIG JOBS

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Network

Network Devices

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Fırat University Renovation of Network Infrastructure Devices - 2016 , 2007

Project Management, Installation, Configuration

2016; Fırat University Project management, installation and configuration tasks were carried out in the main spine, collection point backbone and edge switching devices renovation business.
In this scope, 2 main spine devices, one intermediate spine device in engineering and rector campuses, and 110 edge switching devices for all buildings are configured. The backbone devices are communicated with 40 GB and the edge switching devices are communicated with a 10 GB connection, and all end user terminals are included in the network with 1 GB.

2007; For the collection points at Fırat University, project management, installation and configuration of the edge switching device with 8 backbone devices (for 3 pieces of Fırat Medical Center) and 100+ gigabit ethernet ports were taken. The main vertebrae are connected to each other by 10G and the edge devices are connected to the vertebral via 1G fiber connection.

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Education

Distance Learning

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Fırat University Distance Learning - 2010, 2013, 2017

Project Management, Technical support

In 2010, a distance education infrastructure was created and projected and presented for the first time to users of İLİTAM and Computer Programming.
In 2013, the existing system was replaced with the ALMS distance education system, and server configuration and technical support for the virtual classroom and lms software were provided
In 2017, cloud-based structural transport of the existing system and coordination of the system were coordinated.

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Security

FIREWALL

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Firat University Firewall - 2017

Project Management, Installation, Configuration

Instead of the existing security wall, two active-passive Palo Alto Firewall were involved in the project planning, installation and configuration.

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Wi-Fi

Firat Wi-Fi

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Firat University Wireless internet area - 2013

Project Management, Installation, Configuration

He was tasked with projecting, installing and configuring indoor and outdoor Access Point (AP) devices used at Fırat University. Over 200+ devices are serving to university students and staff in closed and open area. There is expansion possibility to 400 devices. There are over 2,500+ users with 2 controllers in active-active mode.

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CONTACT

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