Assessing the Effectiveness of Various Cybersecurity Approaches in Mitigating Cyber Threat

References

[1] Ahmad, S., Maseleno, A., & Tayyaba, S. (2019). A Survey on Cyber Security Threats and Solutions. Journal of Cybersecurity and Privacy, 1(1), 1-15.

[2] Ahn, J., Cho, D., & Rhee, S. (2019). AI-Powered Cybersecurity: A Survey. IEEE Access, 7, 155155- 155172.

[3] Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Communications Surveys & Tutorials, 17(4), 2347-2376.

[4] Al-Gburi, A. H., Al-Ani, A. K., & Altameemi, A. H. (2020). A Review of Cybersecurity Threats and Mitigation Strategies. International Journal of Advanced Computer Science and Applications, 11(10), 456-465.

[5] Al-Garadi, M. A., Mohammed, A., Al-Ali, A., Du, X., & Guizani, M. (2020). A Survey on IoT Security: Threats, Attacks, and Countermeasures. IEEE Communications Surveys & Tutorials, 22(2), 1076-1104.

[6] Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A Survey. Computer Networks, 54(15), 2787-2805.

[7] Garcia, S., Grill, M., Stiborek, J., & Zunino, A. (2014). Anomaly Detection for Network Security: A Review. Journal of Intelligent Information Systems, 43(3), 571-594.

[8] Hoda, E. M., & Abderrahmane, H. (n.d.). A Survey of Malware Detection Techniques based on Machine Learning. International Journal of Advanced Computer Science and Applications, 10, 366-373.

[9] Humayun, M., Niazi, M., Jhanjhi, N., & Alshayeb, M. (2020). Cybersecurity Threats and Challenges: A Review. Journal of Cybersecurity and Information Systems, 8(1), 1-12.

[10] Kabiri, P., & Ghorbani, A. A. (2005). Research on Intrusion Detection and Response: A Survey. International Journal of Network Security, 1(2), 84-102.

[11] Kolias, C., Kambourakis, G., & Maragoudakis, M. (2017). DDoS in the IoT: A Survey. IEEE Communications Magazine, 55(12), 72-79.

[12] Kawsar, F., & Nakajima, T. (2018). Smart Devices and Interoperability: A Survey. IEEE Access, 6, 15314-15328.

[13] Kortuem, G., Kawsar, F., Fitton, D., & Sundramoorthy, V. (2010). Smart Objects as Building Blocks for the Internet of Things. IEEE Internet Computing, 14(1), 44-51.

[14] Kumar, P., & Choudhary, A. (2019). A Survey on Machine Learning Techniques for Cybersecurity. Journal of Intelligent Information Systems, 54(2), 271-294.

[15] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.

[16] Paxson, V. (1998). Bro: A System for Detecting Network Intruders in Real-Time. Proceedings of the 7th USENIX Security Symposium.

[17] Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context-Aware Computing for the Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1), 414-454.

[18] Rescorla, E., & Modadugu, N. (2012). Datagram Transport Layer Security Version 1.2. RFC 6347.

[19] Roesch, M. (1999). Snort: Lightweight Intrusion Detection for Networks. Proceedings of the 13th USENIX Conference on System Administration.

[20] Roman, R., Zhou, J., & Lopez, J. (2013). Features and Benefits of Smart Devices. IEE

[21] Communication Magazine, 5(10), 104-111.

[22] Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Machine Learning-Based Cybersecurity: A Review. Journal of Cybersecurity and Information Systems, 8(1).1-12.

[23] Kabiri, P., & Ghorbani, A. A. (2005). Research on Intrusion Detection and Response: A Survey. International Journal of Network Security, 1(2), 84-102.

[24] Singh, J., & Singh, J. (2020). A Survey on Machine Learning-based Malware Detection in executable files. Journal of Systems Architecture, 112, 101861.

[25] Snort. (2022). Snort: The Open-Source Network Intrusion Detection System. Retrieved from (link unavailable) (No specific authors, as it's an organization/project)

[26] Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Ding, H., & Wang, C. (2018). Machine Learning and Deep Learning Based Malware Detection. Journal of Intelligent Information Systems, 51(3), 549-566. Sarker, I. H., Kayes, A. S. M., & Watters, P. (2020).

@article{1709325,
      author = {Oketayo Abimbola Mujidat, Oduwole Oluwakemi Omolara, Nriagu Chukwunonso, Bamidele Oluchi Jennie},
      title = {Assessing the Effectiveness of Various Cybersecurity Approaches in Mitigating Cyber Threat},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {12},
      pages = {1792-1797},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709325.pdf},
      abstract = {The increasing sophistication of cyber threats necessitates a comprehensive evaluation of cybersecurity approaches. This study assesses the effectiveness of signature-based detection, anomaly-based detection, artificial intelligence-driven methods, and hybrid models in mitigating cyber threats. A mixed-methods approach is employed, combining quantitative analysis of threat detection rates, response times, and false positive rates with qualitative insights from expert interviews and case studies. The findings reveal the strengths and weaknesses of each approach, highlighting the importance of context-dependent cybersecurity strategies. This research provides actionable recommendations for organizations to enhance their cybersecurity posture, informing the development of adaptive threat mitigation frameworks that integrate multiple approaches. The study's results contribute to the advancement of cybersecurity practices, enabling organizations to better protect themselves against evolving cyber threats.},
      keywords = {Cybersecurity Approaches, Threat Mitigation, Effectiveness Evaluation, Signature-Based Detection, Anomaly-Based Detection, Artificial Intelligence-Driven Methods, Hybrid Models.},
      month = {June},
  }