Effective Cyber Attack Detection Based on Augmented Genetic Algorithm with Naive Bayes

Authors : Hayriye Tanyildiz, Canan Batur Şahin, Özlem Batur Dinler

Pages : 30-35

Doi:10.46572/naturengs.1401669

View : 78 | Download : 70

Publication Date : 2023-12-30

Article Type : Research Paper

Abstract :This study can be considered a vital development in the field of cyber security. Today, the ever-changing and evolving structure of cyber threats constantly challenges defense mechanisms and requires the development of innovative solutions. In this context, the application of the Naive Bayes approach enriched with genetic algorithm offers a significant contribution to existing methodologies in this field. In particular, the use of genetic algorithm in cyber-attack detection optimizes classification processes by determining the most appropriate features from data sets and thus provides a more effective detection mechanism. The integration of the Naive Bayes classifier makes it possible to detect cyber-attacks precisely and quickly based on these selected features. Empirical studies and evaluations have shown that this approach provides superior sensitivity rates and lower false positive rates than traditional techniques, demonstrating its potential to overcome the limitations of existing methods in the field of cybersecurity. These findings can be considered an important step in making cybersecurity strategies more efficient and adaptable, especially considering the constantly evolving and unpredictable nature of cyber threats. The results of this study highlight the importance of developing innovative and effective solutions in the field of cybersecurity and provide a basis for further research in this field.
Keywords : Navie Bayes, Feature Selection, Cyber Attack, Optimisation