Abstract: Examples of the present disclosure describe systems and methods for evaluating malicious web content for associated threats using specialized web crawling techniques. A seed resource identifier is evaluated to determine a second resource identifier associated with the seed resource identifier. A resource corresponding to the second resource identifier is scanned to identify a third resource identifier. The third resource identifier is processed with a machine learning model to classify the third resource identifier according to a classification representing a predicted level of threat. The machine learning model trained to classify resource identifiers into a plurality of classifications. A corrective action can be executed based on the classification of the third resource identifier.

Abstract: Examples of the present disclosure describe systems and methods for evaluating malicious web content for associated threats using specialized web crawling techniques. A seed resource identifier is evaluated to determine a second resource identifier associated with the seed resource identifier. A resource corresponding to the second resource identifier is scanned to identify a third resource identifier. The third resource identifier is processed with a machine learning model to classify the third resource identifier according to a classification representing a predicted level of threat. The machine learning model trained to classify resource identifiers into a plurality of classifications. A corrective action can be executed based on the classification of the third resource identifier.

Abstract: Examples of the present disclosure describe systems and methods for evaluating malicious web content for associated threats using specialized web crawling techniques. In aspects, a first set of malicious and/or potentially malicious resource identifiers is identified. The first set of resource identifiers is evaluated to determine at least a second set of resource identifiers associated with the first set of resource identifiers. The second set of resource identifiers are provide to a web crawling component, which scans the second set of resource identifiers using a threat detection component. If any resource identifiers in the second set of resource identifiers are identified as malicious (or potentially malicious), those resource identifiers may be classified and recorded, provided to the web crawling component, and/or added to the first set of resource identifiers in subsequent threat detection analyses.

Abstract: Examples of the present disclosure describe systems and methods for evaluating malicious web content for associated threats using specialized web crawling techniques. In aspects, a first set of malicious and/or potentially malicious resource identifiers is identified. The first set of resource identifiers is evaluated to determine at least a second set of resource identifiers associated with the first set of resource identifiers. The second set of resource identifiers are provide to a web crawling component, which scans the second set of resource identifiers using a threat detection component. If any resource identifiers in the second set of resource identifiers are identified as malicious (or potentially malicious), those resource identifiers may be classified and recorded, provided to the web crawling component, and/or added to the first set of resource identifiers in subsequent threat detection analyses.