Phishing Attack Detection Model Based the Hybrid Approach to Data Protection in Industrial Control System

Authors

  • E. A. Mityukov Perm National Research Polytechnic University
  • A. V. Zatonsky Perm National Research Polytechnic University, Berezniki Branch

DOI:

https://doi.org/10.14529/ctcr200206

Keywords:

cybersecurity, cyberattacks, industrial control system, anti-phishing, URL features

Abstract

Introduction. Today there is an annual development of phishing techniques cybercriminals who are aimed at industrial control systems in order to compromise sensitive information, the task is to develop new methods for determining phishing attacks aimed at the industrial sector is extremely important. Aim. The article discusses developed method to protection against phishing attacks on users and services of industrial control systems. Materials and methods. The possible literature sources of the subject area are analyzed. Based on the information gathered from previous studies, work on improving the architecture of the phishing protection system are continued. With aimed at improving the accuracy of detection of phishing URLs (Uniform Resource Locator), eight heuristics in the system architecture are added. Most heuristics are aimed at semantic verification of URLs, in terms of the use of special characters, periods, slashes, URL protocols and ports, including the length of the URL itself. Additionally, the validity of the SSL/TLS (Secure Sockets Layer/Transport Layer Security) certificate, phishing keywords in the URL and difference the hosting country of the provider with the country of the top-level domain is checked. Results. The practical research of the new architecture system with various combinations of heuristics are carried out. Quantitative data showing the improvement of key indicators to detecting phishing URLs by the system are presented. Security Officer decides on phishing or legitimate URL by new architecture of system are helped. Conclusion. The presented system shows the following indicators: TPR (True Positive Rate) – 97.85 % and FPR (False positive Rate) – 2.09 %. Also, the accuracy of the method to 98.16 % is improved.

Author Biographies

E. A. Mityukov, Perm National Research Polytechnic University

аспирант кафедры информационных технологий и автоматизированных систем

A. V. Zatonsky, Perm National Research Polytechnic University, Berezniki Branch

д-р техн. наук, профессор, заведующий кафедрой автоматизации технологических процессов

References

Список угроз ФСТЭК. – http://bdu.fstec.ru/threat (дата обращения: 15.02.2020).

Что такое «фишинг» (2019). – https://encyclopedia.kaspersky.ru/knowledge/what-is-phishing/ (дата обращения: 15.12.2019).

Митюков, Е.А. Жизненный цикл фишинговых атак и техники их реализации / Е.А. Митюков // Решение. – 2019. – Т. 1. – С. 140–142.

Митюков, Е.А. Аудит безопасности SCADA-систем / Е.А. Митюков, А.В. Затонский, П.В. Плехов // Защита информации. Инсайд. – 2016. – № 4. – С. 72–77.

Митюков, Е.А. Уязвимости MS SQL SERVER, или использование хранимых процедур в своих целях / Е.А. Митюков // Защита информации. Инсайд. – 2017. – № 6 (78). – С. 44–47.

Спам и фишинг (2018). – https://securelist.ru/spam-and-phishing-in-2018/93453/ (дата обращения: 30.12.2018).

Radiflow Reveals First Documented Cryptocurrency Malware Attack on a SCADA Network (2020). – https://radiflow.com/news/radiflow-reveals-first-documented-cryptocurrency-malware-attackon-a-scada-network/ (дата обращения: 20.01.2020).

Mityukov, E.A. Phishing detection model using the hybrid approach to data protection in industrial control system / E.A. Mityukov, A.V. Zatonsky, P.V. Plekhov, N.V. Bilfeld // IOP Conference Series: Materials Science and Engineering. – 2019. – Vol. 537. DOI: 10.1088/1757-899X/537/5/052014

Gastellier-Prevost Sophie. Decisive heuristics to differentiate legitimate from phishing sites. La Rochelle, France / Gastellier-Prevost Sophie, Granadillo Gustavo Gonzalez, Laurent Maryline // Proc. of conference on network and information systems security (SAR-SSI). – May 2011. – P. 1–9. DOI: 10.1109/SAR-SSI.2011.5931389

Jeeva, S.C. Intelligent phishing URL detection using association rule mining / S.C. Jeeva, E.B. Rajsingh // Human-Centric Comput. Inf. Sci. – 2016. – 6, 10. DOI: 10.1186/s13673-016-0064-3

Sonowal, G. PhiDMA – A phishing detection model with multi-filter approach / G. Sonowal, K.S. Kuppusamy // Journal of King Saud University – Computer and Information Sciences. – Jule 2017. – Vol. 32 (1). – P. 99–112.

Teaching Johnny not to fall for phish / Kumaraguru Ponnurangam, Sheng Steve, Acquisti Alessandro et al. // Article 7 ACM Transactions on Internet Technology. – May 2010. – 10 (2): 31. DOI: 10.1145/1754393.1754396

Затонский, А.В. Информационные технологии: Разработка информационных моделей и систем / А.В. Затонский. – М.: ИЦ Риор, 2014. – 344 с.

A framework for detection and measurement of phishing attacks / S. Garera, N. Provos, M. Chew, A.D. Rubin // Proceedings of the 2007 ACM Workshop On Recurring Malcode. – Alexandria, Virginia, USA, ACM, 2007. – P. 1–8. DOI: 10.1145/1314389.1314391

Zhang Jian. Highly predictive blacklisting / Zhang Jian, Porras Phillip, Ullrich Johannes // Proc. of the 17th conference on security symposium. – CA, USA: USENIX Association Berkeley; 2008. – P. 107–122.

Published

2020-06-07

Issue

Section

Communication Technologies and Systems