Научный журнал Моделирование, оптимизация и информационные технологииThe scientific journal Modeling, Optimization and Information Technology
cетевое издание
issn 2310-6018


Palmov S.V.   Diyazitdinova A.A.   Gubareva O.Y.  

UDC 004.8
DOI: 10.26102/2310-6018/2019.24.1.031

  • Abstract
  • List of references
  • About authors

To simplify the task of ensuring information security is possible through data mining usage. This technology can be used to predict attacks on the information systems. Decision tree is one of the effective tools for predictive models building. Orange is an analytical system that contains a large number of data mining algorithms, including a decision tree. With help of the system made an analysis of real data on network attacks obtained during the experimental study, with the aim of predicting DDoS attacks. Five metrics were used to assess the quality of work: accuracy, specificity, precision, recall and F-measure. The results of the analysis are presented in tabular form. The results were compared with the forecasts created by iWizardE, an intelligent decision support system using a modified decision tree algorithm. iWizard-E surpasses Orange in the first three metrics, but inferior in the last two. The implementation of this algorithm in the Orange and iWizard-E systems cannot be applied to analyze the data of the above type, since they form forecasts with low reliability. It is necessary to improve the decision tree aimed at improving the quality of the generated prognostic models in the context of increasing the values of the “completeness” metric.

1. Enterprise Information Security: Key Threats and Remedies. Available at: http://www.iccwbo.ru/blog/2016/obespechenie-informatsionnoybezopasnosti/ (accessed 01.02.2019). (In Russ.)

2. Mutyala, Nikhil Kumar & Koushik, K.V.s & Sundar, K. John. (2018). Data Mining and Machine Learning Techniques for Cyber Security Intrusion Detection // International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2018. Vol. 3. No. 3. – Pp. 162 – 167. DOI 10.13140/RG.2.2.35197.26085.

3. Gunderman, D. Security Analysts Becoming ‘Data-Mining Gurus’? Q&A With Bay Dynamics’ Ryan Stolte [Электронный ресурс]. – Режим доступа: https://www.cshub.com/attacks/interviews/security-analysts-becoming-datamining-gurus-qa-with-bay-dynamics-ryan-stolte. (дата обращения: 01.02.2019)

4. Palmov, S.V., Miftakhova А.А., Gubareva O. Yu. The Analysis of Telecommunication Traffic by Intelligent Decision Support System // Nauka i biznes: puti razvitiya = Science and Business: Development Ways. 2018. №8 (86). – Pp. 116–122. (In Russ.)

5. Miftakhova, A.A. [Artificial Intelligence for Improving Students’ Performance] // Nauka i biznes: puti razvitiya = Science and Business: Development Ways. 2017. №5 (71). – Pp. 7–12. (In Russ.)

6. DoS and DDoS attacks: meaning and differences. Available at: https://ddosguard.net/ru/info/blog-detail/dos-i-ddos-ataki-znachenie-i-razlichiya (accessed 01.02.2019) (In Russ.)

7. Sharma, Himani & Kumar, Sunil. (2016). A Survey on Decision Tree Algorithms of Classification in Data Mining. International Journal of Science and Research // International Journal of Science and Research. 2016. Vol. 5. No. 4. – Pp. 2094 – 2097.

8. Data Mining Fruitful and Fun. Available at: https://orange.biolab.si/ (accessed 01.02.2019).

9. Informational Entropy. Available at: http://ru.math.wikia.com/wiki/Informatsionnaya_entropiya (accessed 01.02.2019). (In Russ.)

10. Bazhenov, D [Classifier rating (Precision, Recall, F-measure)]. Available at: http://bazhenov.me/blog/2012/07/21/classification-performanceevaluation.html (accessed 01.02.2019). (In Russ.)

Palmov Sergey Vadimovich

Email: psvzo@yandex.ru

Povolzhskiy State University of Telecommunications and Informatics

Samara, Russian Federation

Diyazitdinova Alfiya Askhatovna

Email: miftaxovaaa@mail.ru

Samara State Technical University

Samara, Russian Federation

Gubareva Olga Yuryevna
Candidate of Technical Sciences
Email: olgagubareva@inbox.ru

Samara State Technical University

Samara, Russian Federation

Keywords: artificial intelligence, data mining, orange system, decision making, traffic, f-measure

For citation: Palmov S.V. Diyazitdinova A.A. Gubareva O.Y. TELECOMMUNICATION TRAFFIC ANALYSIS USING ORANGE ANALYTICAL SYSTEM. Modeling, Optimization and Information Technology. 2019;7(1). Available from: https://moit.vivt.ru/wp-content/uploads/2019/01/PalmovSoavtori_1_19_1.pdf DOI: 10.26102/2310-6018/2019.24.1.031 (In Russ).


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