Affiliation:
1. al-Farabi Kazakh National University, Almaty, Kazakhstan
2. Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine
Abstract
This study investigates the future prospects of integration and automation in the active defense of network resources. The main objective of the paper is to evaluate the effectiveness of integrated and automated network resource defense systems in detecting and responding to cyber threats. By analyzing existing technologies, attack modeling, and defense responses, the study validates the importance of integration and automation in reducing threat detection time and improving detection accuracy. The research considers integrated and automated defense systems as the subject and their effectiveness in detecting and responding to cyber threats asthe object of study. The objectives of the work include conducting an analysis of existing integration and automation technologies, modeling attack scenarios and defense responses, and examining the impact of integration and automation on threat detectiontime and accuracy. The results of the study confirm a reduction in threat detection time from 200 minutes to 20 minutes and an increase in detection accuracy from 75% to 95% after the implementation of integration and automation. The findings of the analysis highlight the key role of integration and automation in improving the protection of network resources, making systems more responsive and accurate in detecting and preventing cyberattacks.
Publisher
Abai Kazakh National Pedagogical University
Reference14 articles.
1. [1]Schneier B. Security Engineering: A Guide to Building Dependable Distributed Systems. John Wiley & Sons, 2000.
2. [2]Anderson R. Security Engineering: A Guide to Building Dependable Distributed Systems. John Wiley & Sons, 2008.URL: https://terrorgum.com/tfox/books/security _engineering_ a_guide_to_building_ dependable_distributed_systems.pdf.
3. [3]Mitnick K.D., William L.S. The Art of Deception: Controllingthe Human Element of Security. John Wiley & Sons, 2002.
4. [4]Ng A., Michael I.J. On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. Advances in Neural Information Processing Systems 14 (NIPS 2001), 2002.URL: https://proceedings.neurips.cc/paper_files/paper/2001/file/7b7a53e239400a13bd6be6c91c4f6c4e-Paper.pdf.
5. [5]Bengio Y., Yann L. Scaling learningalgorithms towards AI. Large-Scale Kernel Machines, 2007, pp. 321-356.DOI: https://doi.org/10.7551/mitpress/7496.001.0001.