Cyber Security Assessment and Awareness: A Statistical Modelling Approach

The imperative to combat cybercrime is becoming increasingly crucial, particularly for developing nations like India, presently establishing a robust foundation for safe digitization. This research work endeavors to identify the multifaceted factors contributing to cybercrime, the associated challenges, interrelationships among various cybersecurity variables, potential resolutions, and diverse behavioural attitudes and strategies pertaining to cybercrime prevention. Employing a simple random sampling method, data was collected from 200 individuals on this subject. Principal Component Analysis (PCA) was employed for factor analysis on the dataset to uncover patterns in cyber behavior. Binary Logistic Regression was used to establish the status of being a victim of cybercrime, while a Poisson Regression model was utilized to establish the frequency of cybercrime incidents. The results indicate that the outcome variable, cybercrime victimization, is significantly influenced by independent variables such as identity sharing status, use of a common identity, level of digital security awareness, personal information storage practices online, Downloading complimentary antivirus software from an unfamiliar origin, deactivating antivirus safeguards during downloads, obtaining mathematical material from less-known sources, engaging in clicking activities on links from dubious websites, and sharing personal information with online newcomers. According to the results of the regression model, women exhibit a higher likelihood of experiencing cybercrime compared to men. Cybersecurity knowledge emerges as a key determinant in preventing cyber attacks. Further research, leveraging big data, can offer more robust insights into the underlying determinants contributing to cybercrime victimization. In conclusion, building a digitally secure India entails fostering cybersecurity awareness and cultivating safe online practices.