Enterprise malicious link detection is critical to protecting users and organizations. Research by Proofpoint reveals that 74% of data breaches rely on human-targeted attacks, with clicks on links leading to malicious code execution. Whether to steal credentials in phishing attacks, deploy ransomware, or deliver a malware dropper, adversaries are using links as a key attack vector to initiate their wider campaigns.
In order to carrier lookup tool for developers, security teams need to bolster traditional controls such as DNS filtering and SWGs, email gateways, and browser and OS policies with cutting-edge AI analysis. This defense-in-depth approach ensures that if one layer misses a threat, another will catch it.
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Despite these efforts, many threat actors are finding stealthier ways to hide malicious URLs. In particular, Dark Web actors are offering “FUD Links,” a service that exploits popular public cloud and data-hosting services like GitHub and Content Delivery Networks (CDNs) to host malicious URLs. These FUD Links are used in infection chains for global phishing and malware campaigns.
A state-of-the-art model for detecting malicious URLs employs a custom mixed spatial sequential attention module and a Logically Constrained Neural Network (LCNN). The LCNN is trained to detect malicious patterns by introducing domain-specific logical constraints on character-level URL sequences, enhancing interpretability and robustness against adversarial pattern.
In addition, the model employs character-level embeddings, which are numerical representations of letters that improve contextual understanding and facilitate identifying meaningful patterns such as l o g u r. The model also reads URL characters on a left-to-right basis, similar to the way humans do, and uses word segmentation to reduce sensitivity to punctuation marks, uppercase letters, and other lexical features.