Imunify360 Is A Comprehensive Security Suite For Linux Web Servers

We develop a detection model using black-box type models with the structure and content options to attenuate the danger of adversarial assaults. To validate the proposed model, we design the adversarial attack. We acquire benign documents containing multiple JavaScript codes for the bottom of adversarial samples. We build the adversarial samples by injecting the malware codes into base samples. The proposed mannequin is evaluated in opposition to a big collection of malicious and benign PDFs. We discovered that random forest, an ensemble algorithm of a decision tree, exhibits a good performance on malware detection and is robust for adversarial samples.