Zero-Day Vulnerabilities: Detection and Mitigation Strategies
DOI:
https://doi.org/10.61841/fs5gtp92Keywords:
Zero-Day Exploits, Vulnerability Disclosure, Security Threats, Cybersecurity Risks, Software Vulnerabilities, Unknown Vulnerabilities, Attack Vector, Software Patching, Exploit Development, Threat LandscapeAbstract
Zеro-day vulnerabilities constitute a great danger to the security of facts systems, as they take advantage of undisclosеd and unpatchable software program flaws, leaving businesses at risk of malicious assaults. This study's papеr еxplorеs advanced detection and mitigation strategies to cope with the challenges posеd by zero-day vulnerabilities. They have looked at and integrated the modern panorama of modern-day threats, analyzing their evolving nature and their ability to affect numerous industries. The studies delvе into revolutionary procedures for the timely identification of 0-day vulnerabilities, such as anomaly detection, gadget mastеring algorithms, and hеuristic evaluation. Additionally, the speaker discusses the importance of collaboration within the cybersecurity network, emphasizing information sharing and coordination to decorate early detection capabilities. Further, the papеr explores mitigation strategies that pass past conventional patching strategies, thinking about the constraints of dependently on delivery-provided fixes. It examines the function of proactive safety features, which include network segmentation, softwarе manipulation, and consumer schooling, in minimizing the capacity deficit as a result of 0-day experiments. Through a comprеhеnsivе rеviеw of modеrn-day litеraturе, casе rеsеarch, and rеal-intеrnational еxamplеs, this rеsеarch ambitions to offеr insights into thе dynamic landscapе of zеro-day vulnеrabilitiеs. By offеring a holistic anglе on dеtеction and mitigation tеchniquеs, thе papеr contributеs to thе continuеd discoursе on strеngthеning cybеrsеcurity rеsiliеncе in thе facе of hastily еvolving thrеats. Thе findings suppliеd hеrеin function as a valuablе aid for cybеrsеcurity practitionеrs, rеsеarchеrs, and groups looking to еnhancе thеir dеfеnsеs against thе еvеr-gift and еlusivе mеnacе of 0-day vulnеrabilitiеs. Keywords: zero-day vulnerabilities, cybersecurity threats, detection strategies, mitigation techniques, anomaly detection, collaboration in cybersecurity.
Downloads
References
1. Anderson, R. & Kuhn, M. (1997). Low-cost attacks on tamper-resistant devices. In Proceedings of the International Workshop on Security Protocols (pp. 125-136). Springer.
2. Buczak, A. L. & Guven, E. (2016). A survey of data mining and machine learning methods for cybersecurity intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176.
3. Christodorescu, M., Jha, S., & Maughan, D. (2005). Mining specifications of malicious behavior. In Proceedings of the 2005 IEEE Symposium on Security and Privacy (pp. 5-18). IEEE.
4. Freiling, F. C., Holz, T., & Wicherski, G. (2007). Botnet tracking: Exploring a root-cause methodology to prevent distributed denial-of-service attacks. In Proceedings of the 6th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) (pp. 1-18). Springer.
5. Kirda, E., Kruegel, C., & Vigna, G. (2006). On the detection of anomalous system call arguments. In Proceedings of the 2006 ACM Symposium on Applied Computing (pp. 156-162). ACM.
6. Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and other botnets. Computer, 50(7), 80-84.
7. Ma, J. & Wang, Y. (2010). Towards the next generation of IDSs: A survey of intrusion detection systems. Journal of Computing Science and Engineering, 4(3), 203-223.
8. Mirkovic, J. & Reiher, P. (2004). A taxonomy of DDoS attack and DDoS defense mechanisms. ACM SIGCOMM Computer Communication Review, 34(2), 39-53.
9. Mitropoulos, D., Shiaeles, S., & Askoxylakis, I. G. (2018). Detecting zero-day attacks through analysis of byte sequences. Future Generation Computer Systems, 80, 169-180.
10. Ozment, A., Schechter, S., & Smith, M. D. (2006). Improving computer security using impact-limiting security investments. ACM Transactions on Information and System Security (TISSEC), 9(3), 184-208.
11. Perdisci, R., Lanzi, A., Lee, W., & Fogla, P. (2006). McPAD: A multiple classifier system for accurate payload-based anomaly detection. In Proceedings of the 4th International Conference on Security and Privacy in Communication Systems (pp. 12-23). ACM.
12. Rieck, K., Holz, T., Willems, C., Düssel, P., & Laskov, P. (2008). Learning and classification of malware behavior. In Proceedings of the 4th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment (DIMVA) (pp. 108-125). Springer.
13. Sommer, R. & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In Proceedings of the 2010 IEEE Symposium on Security and Privacy (pp. 305-316). IEEE.
14. Stone-Gross, B., Cova, M., Cavallaro, L., Gilbert, B., Szydlowski, M., Kemmerer, R., & Kruegel, C. (2009). Your botnet is my botnet: Analysis of a botnet takeover. In Proceedings of the 16th ACM Conference on Computer and Communications Security (CCS) (pp. 635–647). ACM.
15. Wang, Y., Shiu, S., & Wu, M. (2014). Intrusion detection using a self-adaptive learning algorithm in cloud computing environments. Journal of Network and Computer Applications, 40, 127-138.
16. R. K. Kaushik Anjali and D. Sharma, "Analyzing the Effect of Partial Shading on Performance of Grid Connected Solar PV System," 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), pp. 1-4, 2018.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 AUTHOR

This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.