Real-time malicious website detection API provides a powerful way to identify websites and links containing malware, phishing, fraud and other exploits. It is used by security tools and applications to protect networks, endpoints, users from websites or links classified as malicious, phishing, fraud, botnet, cryptocurrency mining, ad fraud, etc.

Real-time malicious website detection API in protecting against malicious websites is that the content of such sites is constantly changing and is often hard to detect, especially when it comes to phishing attempts. The use of blacklists can help to mitigate such threats, but since blacklists contain only a subset of all potentially dangerous websites, they cannot be fully effective at detecting new and zero-day attacks.

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Using a combination of blacklists and machine learning, IPQS provides the best of both worlds to ensure accurate and quick malicious URL scanning, phishing check, and malware scan, all while not penalizing legitimate sites with false-positives. This approach is accomplished by leveraging the sequential processing strengths of GRUs and the adversarial training capabilities of GANs to perform a deep, high-speed analysis of suspicious URLs while keeping track of the behavior traits and forensic details of known malware and phishing indicators.

The web threat detection engine also utilizes NLP to analyze the text of a website and look for phishing attempts or misleading information that might lead a user to download malware. In addition, the platform monitors the traffic to a website to identify anomalies that might be indicative of malice, such as unusually high non-human web traffic or large and unexplained changes in traffic patterns.