Holistic Cyber Threat Hunting Using Network Traffic Intrusion Detection Analysis for Ransomware Attacks
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-5091-1_15
Reference13 articles.
1. Connolly LY, Wall DS, Lang M, Oddson B (2020) An empirical study of ransomware attacks on organizations: an assessment of severity and salient factors affecting vulnerability. J Cybersecur 6(1):tyaa023. https://doi.org/10.1093/cybsec/tyaa023
2. Dasgupta D, Akhtar Z, Sen S (2022) Machine learning in cybersecurity: a comprehensive survey. J Def Model Simul 19(1):57–106. https://doi.org/10.1177/1548512920951275
3. Lee JK, Chang Y, Kwon HY et al (2020) Reconciliation of privacy with preventive cybersecurity: the bright internet approach. Inf Syst Front 22:45–57. https://doi.org/10.1007/s10796-020-09984-5
4. Kirda E (2017) Unveil: a large-scale, automated approach to detecting ransomware (keynote). In: 2017 IEEE 24th international conference on software analysis, evolution and reengineering (SANER). IEEE Computer Society, pp 1–1
5. Takeuchi Y, Sakai K, Fukumoto S Detecting ransomware using support vector machines. In: Proceedings of the 47th international conference on parallel processing companion (ICPP ‘18). Association for Computing Machinery, pp 1–6. https://doi.org/10.1145/3229710.3229726
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