Enhancing Cybersecurity Through AI-Powered Solutions: A Comprehensive Research Analysis
Main Article Content
Abstract
This research investigates the tangible quantitative impacts of integrating Artificial Intelligence (AI) into security frameworks. Quantitative analysis reveals a remarkable 30% enhancement in threat detection accuracy and a concurrent 25% reduction in false positives, optimizing resource allocation for threat mitigation. Moreover, the study demonstrates a notable 40% acceleration in identifying and addressing security vulnerabilities, highlighting the efficiency gains enabled by AI technologies. These quantitative findings underscore the substantive advantages of AI in fortifying cybersecurity measures, emphasizing its pivotal role in mitigating evolving threats and improving overall system resilience.
Downloads
Article Details
How to Cite
References
Saxena, D., & Cao, J. (2021). Generative adversarial networks (GANs) challenges, solutions, and future directions. ACM Computing Surveys (CSUR), 54(3), 1-42.
Sampath, V., Maurtua, I., Aguilar Martin, J. J., & Gutierrez, A. (2021). A survey on generative adversarial networks for imbalance problems in computer vision tasks. Journal of big Data, 8, 1-59.
Chen, H. (2021, March). Challenges and corresponding solutions of generative adversarial networks (GANs): a survey study. In Journal of Physics: Conference Series (Vol. 1827, No. 1, p. 012066). IOP Publishing.
Salehi, P., Chalechale, A., & Taghizadeh, M. (2020). Generative adversarial networks (GANs): An overview of theoretical model, evaluation metrics, and recent developments. arXiv preprint arXiv:2005.13178.
Hwang, U., Jung, D., & Yoon, S. (2019, May). Hexagan: Generative adversarial nets for real world classification. In International conference on machine learning (pp. 2921-2930). PMLR.
Barnett, S. A. (2018). Convergence problems with generative adversarial networks (gans). arXiv preprint arXiv:1806.11382.
Manisha, P., & Gujar, S. (2018). Generative Adversarial Networks (GANs): What it can generate and What it cannot?. arXiv preprint arXiv:1804.00140.
Pan, Z., Yu, W., Yi, X., Khan, A., Yuan, F., & Zheng, Y. (2019). Recent progress on generative adversarial networks (GANs): A survey. IEEE access, 7, 36322-36333.
Wang, Z., She, Q., & Ward, T. E. (2021). Generative adversarial networks in computer vision: A survey and taxonomy. ACM Computing Surveys (CSUR), 54(2), 1-38.
Pan, Z., Yu, W., Wang, B., Xie, H., Sheng, V. S., Lei, J., & Kwong, S. (2020). Loss functions of generative adversarial networks (GANs): Opportunities and challenges. IEEE Transactions on Emerging Topics in Computational Intelligence, 4(4), 500-522.
Gonog, L., & Zhou, Y. (2019, June). A review: generative adversarial networks. In 2019 14th IEEE conference on industrial electronics and applications (ICIEA) (pp. 505-510). IEEE.
Toshpulatov, M., Lee, W., & Lee, S. (2021). Generative adversarial networks and their application to 3D face generation: A survey. Image and vision computing, 108, 104119.
Jabbar, A., Li, X., & Omar, B. (2021). A survey on generative adversarial networks: Variants, applications, and training. ACM Computing Surveys (CSUR), 54(8), 1-49.
Arora, A., & Arora, A. (2022). Generative adversarial networks and synthetic patient data: current challenges and future perspectives. Future Healthcare Journal, 9(2), 190.
Vo, D. M., Nguyen, D. M., Le, T. P., & Lee, S. W. (2021). HI-GAN: A hierarchical generative adversarial network for blind denoising of real photographs. Information Sciences, 570, 225-240.
Johnson, P., & Smith, R. (2021). The Role of Artificial Intelligence in Cybersecurity: Applications, Implications, and Challenges. Springer.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444.
Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep Learning. MIT Press.
Kamaraju, P., Kolhe, J., & Mohanta, D. (2020). A comprehensive study on deep learning in cybersecurity. International Journal of Computational Intelligence Systems, 13(1), 1169-1184.
Xie, W., Yu, X., & Gao, Y. (2019). A Survey on Deep Learning Based Cybersecurity Defense Mechanisms. IEEE Access, 7, 156846-156859.