Web Security in the Digital Age

The hazards associated with cyber security attacks have increased due to our increasing reliance on digital technologies. We suggest an ensemble strategy employing multiple models of machine learning to get over these restrictions. Our suggested approach detects an extra 141 dangerous URLs, outperforming the single model that is currently in use by 6%. We gathered a collection of malicious and benign URLs for this investigation. Applying Principal Component Analysis (PCA) to feature extraction. Subsequently advantageous features are selected and categorised using the Social Spider optimised Densely-connected Convolutional Caps net (SSO-DCN). The most effective detection frameworks and automated methods for identifying rogue web pages, including those distribute malicious malware, are included in the proposed technology. When comparing the proposed method to the existing approaches, the improvements are in mean accuracy (96.30%), precision (96.90%), F1-Measure (97.20%), recall (96.90%), FPR (3.50%) and FNR (3.90%).