[
1. Widyaninsih, P., D. R. S. Saputro, A. W. Nugroho. Susceptible Exposed Infected Recovery (SEIR) Model with Immigration: Equilibria Points and Its Application. – In: Proc. of ICSAS, 2018, AIP Conf. 2014, 020165-1-020165-7. https://doi.org/10.1063/1.5054569, https://aip.scitation.org/doi/pdf/10.1063/1.5054569
]Search in Google Scholar
[
2. Chen, Z. Modeling and Defending against Internet Worm Attacks. Doctor of Philosophy Thesis, School of Electrical and Computer Engineering, Georgia Institute of Technology, May 2007.
]Search in Google Scholar
[
3. Mishra, B. K., A. Prajapati. Cyber Warfare: Worms’ Transmission Model. – International Journal of Advanced Science and Technology, Vol. 63, 2014, pp. 83-94. http://dx.doi.org/10.14257/ijast.2014.63.0810.14257/ijast.2014.63.08
]Search in Google Scholar
[
4. Joseph, A. F., S. EAdewumi, Ih. Olalekan, K. Sunday. Computer Viruses: A Framework for Modeling Infection Susceptibility of Workstations. – Advances in Computer Science and Engineering, Vol. 14, 2015, No 2 pp. 97-109. DOI: 10.17654/ACSEMay2015_097_109.
]Open DOISearch in Google Scholar
[
5. Vetrivelan, P., M. Jagannath, T. S. Pradeep Kumar. Network Intrusion Detection and Prevention Systems on Flooding and Worm Attacks. Combating Security Breaches and Criminal Activity in the Digital Sphere Chapter: Network Intrusion Detection and Prevention Systems on Flooding and Worm Attacks, Publisher: IGI Global. Y. Asnath Victy Phamila, S. Geetha, Eds. June 2016. DOI: 10.4018/978-1-5225-0193-0.
]Open DOISearch in Google Scholar
[
6. Liu, L., R. K. L. Ko, G. Ren, X. Xu. Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks. – Security and Communication Networks, Vol. 2017, Article ID 2910310. 8 p. https://doi.org/10.1155/2017/291031010.1155/2017/2910310
]Search in Google Scholar
[
7. Gowtham, K., N. Sricharan, R. Kisore. Mathematical Model to Study Propagation of Computer Worm in a Network. – In: Proc. of IEEE International Advance Computing Conference (IACC’15), June 2015, pp. 12-13. DOI: 10.1109/IADCC.2015.7154812.
]Open DOISearch in Google Scholar
[
8. Bradley, J. T., S. Gilamore. Analyzing Distributed Internet Worm Attacks Using Continuous State-Space Approximation of Process Algebra Models. – Journal of Computer and System Sciences, Vol. 74, 2008, pp. 1013-1032.10.1016/j.jcss.2007.07.005
]Search in Google Scholar
[
9. Tang, Y., J. Luo, B. Xiao, G. Vei. Concept, Characteristics and Defending Mechanism of Worms, Special Section on Information and Communication System Security. – IEICE Trans. Inf. & Syst., Vol. E92–D, 2009, No 5. http://www4.comp.polyu.edu.hk/~csbxiao/paper/2009/IEICE-2009-worm.pdf10.1587/transinf.E92.D.799
]Search in Google Scholar
[
10. Sidiroglou, St., A. D. Keromytis. A Network Worm Vaccine Architecture. – In: Proc. of 12th International Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE’03, June 2003. https://dl.acm.org/doi/10.5555/938984.939795
]Search in Google Scholar
[
11. Kamal, S. U. M., R. J. A. Ali, H. K. Alani, E. S. Abdulmajed. Survey and Brief History on Malware in Network Security Case Study: Viruses, Worms, and Bots. – ARPN Journal of Engineering and Applied Sciences, Vol. 11, 2016, No 1, pp. 683-698.
]Search in Google Scholar
[
12. Masthan, M., R. Ravi. Detection and Prevention of Unknown Vulnerabilities on Enterprise IP Networks. – International Journal of Computer Science and Mobile Computing, Vol. 4, 2015, No 10, pp. 343-352.
]Search in Google Scholar
[
13. Souissi, S., A. Serhrouchni. AIDD: A Novel Generic Attack Modeling Approach. – In: Proc. of International Conference on High Performance Computing, Simulation (HPCS’14), July 2014, Bologne, Italy, 2014. 〈10.1109/HPCSim.2014.6903738〉. 〈hal01205824〉.10.1109/HPCSim.2014.6903738
]Search in Google Scholar
[
14. Chapter 16 Attack Detection and Prevention. National Security Telecommunications Advisory Committee (NSTAC) Intrusion Detection Subgroup, [NetSec], WS 2006/2007. http://www.ccs-labs.org/~dressler/teaching/netzwerksicherheitws0607/16_AttackDetection-v2.pdf
]Search in Google Scholar
[
15. Bajaj, P., A. G. Roy. Source Code Analysis of Worms. http://www.micsymposium.org/mics_2004/Bajaj.pdf
]Search in Google Scholar
[
16. Mueller, P., B. Yadegari. The Stuxnet Worm. https://www2.cs.arizona.edu/~collberg/Teaching/466-566/2012/Resources/presentations/topic9-final/report.pdf
]Search in Google Scholar
[
17. Dimov, D., Y. Tzonev. Pass-the-Hash: One of the Most Prevalent Yet Underrated Attacks for Credentials Theft and Reuse. – In: Proc. of ACM International Conference Proceeding Series, Part F132086, 2017, pp. 149-154. ISBN 978-145035234-5. DOI: 10.1145/3134302.3134338.
]Open DOISearch in Google Scholar
[
18. Dimov, D., Y. Tzonev. Result Oriented Time Correlation between Security and Risk Assessments, and Individual Environment Compliance Framework. – In: Proc. of International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning Smart Innovation, Systems and Technologies, 2019, pp. 373-383. ISBN 978-3-030-03576-1. DOI: 10.1007/978-3-030-03577-8_42.
]Open DOISearch in Google Scholar
[
19. Dimov, D., Y. Tzonev. Observing, Measuring and Collecting HDD Performance Metrics on a Physical Machine during Ransomware Attack. – Information & Security: An International Journal, Vol. 47, 2020, No 3, pp. 317-327. ISSN 0861-5160. DOI: 10.11610/isij.4723.
]Open DOISearch in Google Scholar
[
20. Popchev, I., D. Orozova. Towards Big Data Analytics in the e-Learning Space. – Cybernetics and Information Technologies, Vol. 19, 2019, No 3, pp. 16-24.10.2478/cait-2019-0023
]Search in Google Scholar
[
21. Gnanaprasanambikai, L., N. Munusamy. Towards Big Data Analytics Data Pre-Processing and Classification for Traffic Anomaly Intrusion Detection Using NSLKDD Dataset. – Cybernetics and Information Technologies, Vol. 18, 2018, No 3, pp. 111-119.10.2478/cait-2018-0042
]Search in Google Scholar