Making the Undetectable Detectable: How Noname Security Uses Machine Learning to Spot API Anomalies

In this blog, we will learn how Noname Security Uses Machine Learning to Spot API Anomalies.

Introduction:

In today’s digital-first world, APIs are everywhere, fueling mobile apps, enabling financial transactions, driving healthcare innovation, and more. As organizations increasingly rely on APIs, so do cybercriminals, who view APIs as high-value targets. Detecting anomalous behavior hidden within massive volumes of API traffic is now a critical priority.

Traditional security tools are no longer sufficient. Static rules and known threat signatures struggle to keep pace with ever-evolving threats. That’s where Noname Security’s machine learning-based anomaly detection steps in delivering intelligence that adapts to the organization’s environment, detects the unexpected, and helps stop attacks in real time.

Why Machine Learning Is Critical for API Anomaly Detection

Unlike typical web traffic, APIs generate vast numbers of machine-to-machine calls. A single page load on a web app may trigger dozens of API calls behind the scenes. This high volume makes it easy for malicious requests to hide within legitimate traffic especially when attackers mimic normal usage patterns.

Machine learning (ML) enables Noname Security to cut through the noise. It builds a behavioral baseline for every API, user, and system it observes, identifying deviations that suggest malicious intent such as unauthorized access attempts, unusual request frequency, or data exfiltration.

How Noname Security’s ML Engine Works

Noname’s anomaly detection is powered by an unsupervised learning model. Here’s how it functions:

Learning Phase: Each API undergoes a learning period, during which the system observes and models normal behavior across users, endpoints, request types, and data volumes.

Baseline Establishment: ML models consider multi-feature patterns, such as IP geolocation, response codes, payload size, frequency, and user agents, to define a baseline of what “normal” looks like.

Real-Time Detection: Once the baseline is set, the system can detect behavioral anomalies, e.g., skipping steps in workflows, accessing data unusually fast, or triggering high error rates.

Response & Alerting: Anomalies are flagged within 30–60 seconds of detection. Severity and sensitivity settings are customizable per organization.

Continuous Adaptation: The model adjusts to evolving behavior and can learn from marked false positives to reduce alert fatigue.

What Types of Anomalies Can It Detect?

The Noname Security platform detects both pattern-based and behavior-based anomalies:

Pattern-Based Anomalies:

• Known web exploitation techniques (e.g., SQL injection, Command Injection, Spring4Shell, etc.)
• Suspicious user agents and malformed payloads.
• High error rates and unusual response codes.

Behavior-Based Anomalies:

• Excessive API usage or scraping behavior.
• Accessing unexpected fields or skipping expected steps (e.g., skipping payment in a checkout flow)
• Attempts to use elevated permissions or access unauthorized resources.
• Using an API in an abnormal sequence or at unusual times.

These insights allow Noname to detect everything from bot-driven credential stuffing and brute-force attempts to insider misuse and advanced persistent threats.

AI That Understands API Intent

Noname Security goes beyond traditional machine learning by leveraging a dedicated Large Language Model (LLM). This LLM is purpose-built for API security and works by analyzing API metadata—including the host, path, method, and learned schema—to intelligently infer the intended use of each API endpoint.

By understanding the purpose behind API calls, Noname enhances its ability to detect anomalies, flag unintended data exposure, and spot misuse that might otherwise appear normal in surface-level traffic analysis.

This LLM-powered analysis enables:

• A deeper context of what each API is designed to do
• Improved detection accuracy for complex attack patterns
• Fewer false positives through intent-based validation
• Smarter runtime protection without relying solely on static signatures

By combining behavioral ML and LLM-driven insights, Noname delivers best-in-class anomaly detection that’s context-aware, adaptive, and built for modern API ecosystems.

Final Thoughts

APIs aren’t just technical enablers, they’re strategic business assets. As attackers grow more sophisticated, the ability to detect subtle behavioral anomalies at scale is no longer a luxury. It’s a necessity.

With Noname Security’s machine learning-powered anomaly detection, organizations gain the upper hand, ensuring threats are identified faster, investigated smarter, and mitigated effectively.

In the evolving world of API security, it’s not about blocking every request. It’s about understanding every request and detecting the ones that don’t belong.

Contact our team at sales@pronteff.com to learn how Noname can protect your APIs and your business.