HONEYPOT TECHNOLOGIES FOR MALWARE DETECTION AND ANALYSIS

Authors

  • Dragoș DRĂGHICESCU Research staff, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest
  • Alexandru CARANICA Research staff, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest
  • Octavian FRATU Professor, Faculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest

DOI:

https://doi.org/10.53477/2668-2028-21-34

Keywords:

Keywords: computer security; honeypot technologies; malware analysis.

Abstract

Abstract: In this paper, we offer a brief summary of latest developments in honeypot technologies, used for malware detection and analysis. This includes not only honeypot software, but also methodologies to analyze captured honeypot data. As such, our focus in this work is to keep track of current developments related to traffic analysis, especially honeypot technologies, as a means of data capture and interpretation of malicious traffic. Zero-day attacks are still very hard to predict, then handle, by any security platform. Means to successfully predict an attack is of paramount importance to the world of cybersecurity. Effective network security administration depends, to a great extent, on the understanding of existing and emerging threats propagated over the web. In order to protect information systems and its users, it is of crucial importance to collect accurate, concise, high-quality information about malicious activities, for security researchers to be able to reverse-engineer, then understand and stop a malicious actor.

References

1. DV Silva, GDR Rafael, “A review of the current state of Honeynet architectures and tools”, International Journal of Security and Networks 12 (4), 255-272, 2017.
2. BEHNAM Anisi, “State-of-the-art Evaluation of Low and Medium Interaction honeypots for Malware Collection”, Degree Thesis, Edinburgh Napier University, School of Computing, Aug 2016.
3. ASONGANYI Jeffrey Nkwetta, “Honey-System: Design, Implementation and Attack Analysis”, College of Technology, University of Buea, in partial fulfilment of the requirements for the award of the Degree of Bachelor of Technology, 2018.
4. “Intrusion Detection System,” Wikipedia.org, https://en.wikipedia.org/wiki/Intrusion_ detection_ system, accessed February 2021.
5. “Zero-day (computing),” Wikipedia.org, https://en.wikipedia.org/wiki/Zeroday_ (computing), accessed February 2021.
6. PAGE Carly, “The first M1 MacBook malware has arrived – here's what you need to know”, TechRadar https://www.techradar.com/news/the-first-m1-macbook-malware-has-arrived-heres-what-you-need-to-know, accessed February 2021.
7. “Honeypot (computing),” Wikipedia.org, https://en.wikipedia.org/wiki/Honeypot_ (computing), accessed February 2021.
8. ZHOU Z., CHEN Z., ZHOU T. and GUAN X., "The study on network intrusion detection system of Snort," 2010 International Conference on Networking and Digital Society, Wenzhou, China, 2010, doi: 10.1109/ICNDS.2010.5479341.
9. Honeynet Project, Wikipedia: https://en.wikipedia.org/wiki/Honeynet_Project, accessed February 2021.
10. KUWATLY I., SRAJ M., AL MASRI Z. and ARTAIL H., A dynamic honeypot design for intrusion detection, In Pervasive Services, ICPS 2004, IEEE/ACS International Conference on IEEE, 2004
11. SPITZNER L., The honeynet project: Trapping the hackers, IEEE Security&Privacy, 1(2), 2003.
12. MOKUBE I. and ADAMS M., Honeypots: concepts, approaches, and challenges, In Proceedings of the 45th annual southeast regional conference. ACM, 2007.
13. MAIRH A., BARIK D., VERMA K. and JENA D., Honeypot in network security: a survey, In Proceedings of the 2011 international conference on communication, computing&security, ACM, 2011.
14. HUANG P. S., YANG C. H. and AHN T. N., Design and implementation of a distributed early warning system combined with intrusion detection system and honeypot, In Proceedings of the 2009 International Conference on Hybrid Information Technology, ACM, 2009.
15. ANSONA N, Dr. BABU S. Sasidhar, SHEEMA M., Prof. JAYAKUMAR P., Integrated Honeypot, IJCET, 2014.
16. HUANG Linan, ZHU Quanyan, Game of Duplicity: A Proactive Automated Defense Mechanism by Deception Design. arXiv:2006.07942 [cs.GT], 2020-06-14.
17. ADENIJI Oluwashola David, OLATUNJI Oluwadare Oluwasola, Zero Day Attack Prediction with Parameter Setting Using Bi Direction Recurrent Neural Network in Cyber Security. International Journal of Computer Science and Information Security (IJCSIS), Vol. 18, No. 3, March 2020.
18. NAWROCKI Marcin, WAHLISCH Matthias, SCHMIDT C. Thomas, KEIL Christian, SCHONFELDER Jochen, A Survey on Honeypot Software and Data Analysis, 22 August 2016.

Downloads

Published

2021-08-12

Issue

Section

Articles