BibTex RIS Kaynak Göster

Kullanıcıların Günlük Davranışına Bağlı Olarak Akademik Ağların Genel Örüntüsünün Belirlenmesi

Yıl 2018, Cilt: 8 Sayı: 1, 203 - 210, 01.01.2018

Fidan Kaya Gülağız Onur Gök Suhap Şahin

Öz

Teknolojideki gelişmeler ile birlikte internet kullanımı da yaygınlaşmıştır bu durumun bir sonucu olarak veriler de elektronik ortama taşınmıştır. Elektronik ortamda saklanan verinin boyutunun artması ile birlikte verilerin güvenliğinin sağlanması daha da önemli hale gelmiştir. Bu nedenle ağda meydana gelen anormalliklerin ve saldırıların erken teşhisi önemlidir. Ağdaki anormallikleri tespit etmek amacıyla kullanılan pek çok farklı veri madenciliği yöntemi mevcuttur. Bu çalışmada akademik ağlardaki anormallikleri tespit etmek amacıyla ağın genel davranışı tanımlanmıştır. Bu amaçla Iterative K-Means ve Hidden Markov Model HMM yöntemlerini kullanan bir ağ durum analizi yöntemi önerilmiştir

Anahtar Kelimeler

Akademik ağlar Atak tespiti Hidden markov model

Kaynakça

  • Alpaydın, E. 2013. Yapay Öğrenme. Boğaziçi Üniversitesi Yayınevi, İstanbul.
  • Dorj, E., Altangerel, E. 2013. Anomaly detection approach using hidden markov model. International Forum on Strategic Technology, s.1-4, Ulaanbaatar, Mongolia.
  • Fan, W. K. G. 2012. An adaptive anomaly detection of WEB- based attacks. 7th International Conference on Computer Science & Education, s. 690-694, Melbourne, Australia.
  • Foltz, C. B. 2004. Cyberterrorism, computer crime and reality. Info. Mngmt. & Comp. Sec.12: 154-166.
  • Garsva, E., Paulauskas, N., Grazulevicius, G., Gulbinovic, L. 2014. Packet inter-arrival time distribution in academic computer network. Elektron. Electrotech. 20: 87-90.
  • Hong, B., Hu, Y., Peng, F., Deng, B. 2015. Distributed state monitoring for IaaS Cloud with continuous observation sequence. IEEE 15th Conference on Scalable Computing and Communications and Its Associated Workshops, s. 1037-1042, Beijing, China.
  • Jain, R., Abouzakhar, N. S. 2012. Hidden markov model based anomaly intrusion detection. International Conference for Internet Technolog and Secured Transactions, s. 528-533, London, United Kingdom.
  • Karthick, R. R., Hattiwale,V. P., Ravindran, B. 2012. Adaptive network intrusion detection system using a hybrid approach. Fourth International Conference on Communication Systems and Networks, s. 1-7, Bangalore, India.
  • Kaya Gülağız, F. , Şahin, S. 2017. Comparison of hierarchical and non-hierarchical clustering algorithms. Int. J. of Comp. Eng. and Info.Tech., 9: 6-14.
  • Kaya, H., Köymen, K. 2008. Veri madenciliği kavrami ve uygulama alanlari. Doğu Anadolu Bölgesi Araştırmları, s. 159-164.
  • OPNET Technologies 1986, Optimum network simulation and engineering tool. https:// www.riverbed.com/ gb/products/ steelcentral/ opnet.html?redirect=opnet
  • Ramage, D. 2007. http://cs229.stanford.edu/section/cs229-hmm.pdf
  • Sultana, A., Hamou-Lhadj, A., Couture, A. 2012. An improved hidden markov model for anomaly detection using frequent common patterns. IEEE International Conference on Communications (ICC),s. 1113-1117, Ottawa, ON, Canada.

Identifying the general pattern of the academic computer networks based on users daily behaviors

Yıl 2018, Cilt: 8 Sayı: 1, 203 - 210, 01.01.2018

Fidan Kaya Gülağız Onur Gök Suhap Şahin

Öz

The use of the internet has become wide spread with the developments in technology as a result of this data has been removed to electronic environment. With the increase of data stored in the electronic environment, the security of the data has become much important. For this reason, network anomalies and attacks should be detected early. There are many different data mining methods used to detect network anomalies. In this study general behavior of academic networks determined to detect network anomalies. For this purpose, a network state analysis method using Iterative K-Means and Profile Hidden Markov Model PHMM methods is proposed.

Anahtar Kelimeler

Academic Networks Intrusion Detection Profile Hidden Markov Model

Kaynakça

  • Alpaydın, E. 2013. Yapay Öğrenme. Boğaziçi Üniversitesi Yayınevi, İstanbul.
  • Dorj, E., Altangerel, E. 2013. Anomaly detection approach using hidden markov model. International Forum on Strategic Technology, s.1-4, Ulaanbaatar, Mongolia.
  • Fan, W. K. G. 2012. An adaptive anomaly detection of WEB- based attacks. 7th International Conference on Computer Science & Education, s. 690-694, Melbourne, Australia.
  • Foltz, C. B. 2004. Cyberterrorism, computer crime and reality. Info. Mngmt. & Comp. Sec.12: 154-166.
  • Garsva, E., Paulauskas, N., Grazulevicius, G., Gulbinovic, L. 2014. Packet inter-arrival time distribution in academic computer network. Elektron. Electrotech. 20: 87-90.
  • Hong, B., Hu, Y., Peng, F., Deng, B. 2015. Distributed state monitoring for IaaS Cloud with continuous observation sequence. IEEE 15th Conference on Scalable Computing and Communications and Its Associated Workshops, s. 1037-1042, Beijing, China.
  • Jain, R., Abouzakhar, N. S. 2012. Hidden markov model based anomaly intrusion detection. International Conference for Internet Technolog and Secured Transactions, s. 528-533, London, United Kingdom.
  • Karthick, R. R., Hattiwale,V. P., Ravindran, B. 2012. Adaptive network intrusion detection system using a hybrid approach. Fourth International Conference on Communication Systems and Networks, s. 1-7, Bangalore, India.
  • Kaya Gülağız, F. , Şahin, S. 2017. Comparison of hierarchical and non-hierarchical clustering algorithms. Int. J. of Comp. Eng. and Info.Tech., 9: 6-14.
  • Kaya, H., Köymen, K. 2008. Veri madenciliği kavrami ve uygulama alanlari. Doğu Anadolu Bölgesi Araştırmları, s. 159-164.
  • OPNET Technologies 1986, Optimum network simulation and engineering tool. https:// www.riverbed.com/ gb/products/ steelcentral/ opnet.html?redirect=opnet
  • Ramage, D. 2007. http://cs229.stanford.edu/section/cs229-hmm.pdf
  • Sultana, A., Hamou-Lhadj, A., Couture, A. 2012. An improved hidden markov model for anomaly detection using frequent common patterns. IEEE International Conference on Communications (ICC),s. 1113-1117, Ottawa, ON, Canada.
Toplam 13 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Bölüm Research Article
Yazarlar

Fidan Kaya Gülağız Bu kişi benim

Onur Gök Bu kişi benim

Suhap Şahin Bu kişi benim

Yayımlanma Tarihi 1 Ocak 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 1

Kaynak Göster

APA Gülağız, F. K., Gök, O., & Şahin, S. (2018). Identifying the general pattern of the academic computer networks based on users daily behaviors. Karaelmas Fen Ve Mühendislik Dergisi, 8(1), 203-210.
AMA Gülağız FK, Gök O, Şahin S. Identifying the general pattern of the academic computer networks based on users daily behaviors. Karaelmas Fen ve Mühendislik Dergisi. Ocak 2018;8(1):203-210.
Chicago Gülağız, Fidan Kaya, Onur Gök, ve Suhap Şahin. “Identifying the General Pattern of the Academic Computer Networks Based on Users Daily Behaviors”. Karaelmas Fen Ve Mühendislik Dergisi 8, sy. 1 (Ocak 2018): 203-10.
EndNote Gülağız FK, Gök O, Şahin S (01 Ocak 2018) Identifying the general pattern of the academic computer networks based on users daily behaviors. Karaelmas Fen ve Mühendislik Dergisi 8 1 203–210.
IEEE F. K. Gülağız, O. Gök, ve S. Şahin, “Identifying the general pattern of the academic computer networks based on users daily behaviors”, Karaelmas Fen ve Mühendislik Dergisi, c. 8, sy. 1, ss. 203–210, 2018.
ISNAD Gülağız, Fidan Kaya vd. “Identifying the General Pattern of the Academic Computer Networks Based on Users Daily Behaviors”. Karaelmas Fen ve Mühendislik Dergisi 8/1 (Ocak 2018), 203-210.
JAMA Gülağız FK, Gök O, Şahin S. Identifying the general pattern of the academic computer networks based on users daily behaviors. Karaelmas Fen ve Mühendislik Dergisi. 2018;8:203–210.
MLA Gülağız, Fidan Kaya vd. “Identifying the General Pattern of the Academic Computer Networks Based on Users Daily Behaviors”. Karaelmas Fen Ve Mühendislik Dergisi, c. 8, sy. 1, 2018, ss. 203-10.
Vancouver Gülağız FK, Gök O, Şahin S. Identifying the general pattern of the academic computer networks based on users daily behaviors. Karaelmas Fen ve Mühendislik Dergisi. 2018;8(1):203-10.