Paper Details

Name: Dipak Vijay Rajput
Country: India

Year: 2026
Volume: Volume No: 13, Issue No: 1 (January-June)
Page Number: 142-154
DOI: https://doi.org/10.5281/zenodo.20522010

ABSTRACT
The rapid growth of digital technologies and internet-based systems has increased cybersecurity challenges across organizations worldwide. Cyber threats such as malware attacks, phishing, ransomware, data breaches, and unauthorized access have highlighted the need for effective cybersecurity models. However, evaluating the efficiency and reliability of these models remains a major challenge due to the lack of standardized analytical frameworks. Therefore, this study focuses on the performance evaluation of cybersecurity models using statistical and analytical techniques. The primary purpose of the research is to analyze the effectiveness of traditional and advanced cybersecurity models through quantitative and analytical approaches. The study adopted a descriptive and analytical research methodology using both primary and secondary data sources. Primary data were collected from 100 respondents, including IT professionals and cybersecurity experts, through structured questionnaires and expert opinions, while secondary data were obtained from journals, reports, and research publications. Statistical techniques such as regression analysis, ANOVA, correlation analysis, and hypothesis testing were used to evaluate cybersecurity model performance based on key indicators including accuracy, detection rate, response time, scalability, and reliability. The findings revealed that AI and Machine Learning-based cybersecurity models demonstrated higher detection accuracy and faster response time compared to traditional signature-based and rule-based systems. Cloud-based security models also showed significant scalability and reliability advantages. The study contributes to cybersecurity research by integrating statistical evaluation methods into cybersecurity performance assessment and provides practical recommendations for organizations to strengthen cybersecurity strategies and improve digital protection systems.

Keywords: Cybersecurity, Model Evaluation, Statistical Analysis, Performance Metrics, Data Analytics.