Culturally adaptive neural network for detecting cybersecurity vulnerabilities in Bangladeshi web applications

Abstract

As cyber threats become more complex and frequent, conventional methods for detecting website vulnerabilities, such as rule-based and heuristic approaches, faces significant difficulties, including limited adaptability, high rates of false positives, and a lack of contextual insight. This study presents a predictive model based on neural networks aimed to actively evaluating website security. By applying essential features like security headers, SSL/TLS settings, and SQL injection vulnerabilities, the model detects complex patterns and irregularities, enabling precise identification of emerging threats and vulnerabilities. This approach uses data-driven feature engineering and training with custom neural architectures, for comparison we used random forest and gradient boosting, For explainability we used SHAP followed by evaluation metrics such as precision, recall, and F1-score. Key results show improved accuracy, reduction of false positives, automated monitoring of configurations, and enhancement of resilience against adversarial attacks. Although neural networks show significant potential for transformation, challenges related to transparency, computational demands, and data imbalance are acknowledged. This highlights the necessity for ongoing learning, scalability, and integration with current frameworks, laying the groundwork for robust and adaptable web security strategies.