COMPUTER SECURITY EDUCATION AND RESEARCH HANDLE WITH CARE
Аннотация
In our thesis, we explained honeypot systems in detail, and implemented low interaction, middle interaction and high interaction honeypots at laboratory. Our goal was to understand their strategy and how they are working in order to lure intruders towards the system.
Библиографические ссылки
GDataGData, MalwareNumbers, 017, http://www.gdatasoftware.com.
P. Owezarski, “Unsupervised classification and characterization of honeypot attacks,” in Proceedings of 10th International Conference on Network and Service Management (CNSM) and Workshop, pp. 10–18, Rio de Janeiro, Brazil, November 2014.View at: Publisher Site | Google Scholar
S. Dowling, M. Schukat, and E. Barrett, “Improving adaptive honeypot functionality with efficient reinforcement learning parameters for automated malware,” Journal of Cyber Security Technology, vol. 2, no. 2, pp. 75–91, 2018.View at: Google Scholar
I. M. M. Matin and B. Rahardjo, “Malware detection using honeypot and machine learning,” in Proceedings of 2019 7th International Conference on Cyber and IT Service Management (CITSM), pp. 1–4, Bandung Institute of Technology, Bandung, Indonesia, November 2019.View at: Google Scholar
L. Spitzner, Honeypots: Tracking Hackers, Addison-Wesley, Clemson, SC, USA, 2003.
T. Luo, Z. Xu, X. Jin, Y. Jia, and X. Ouyang, “Iotcandyjar: towards an intelligent-interaction honeypot for iot devices,” in Proceedings of the Black Hat, Las Vegas, NV, USA, 2017.View at: Google Scholar