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Cymulate is a SaaS-based breach and attack simulation platform that makes it simple to know and optimize your security posture any time, all the time, and empowers companies to safeguard …
The NodeZero™ platform empowers organizations to continuously find, fix, and verify exploitable attack surfaces. It is the flagship product of Horizon3.ai, founded in 2019 by former industry and U.S. National …
AttackIQ was a leading independent vendor of breach and attack simulation solutions, built the industry’s first Security Optimization Platform for continuous security control validation and improving security program effectiveness and …
Cobalt’s crowdsourced application security solution transforms today’s broken pen testing model into a data driven engine fueled by their global talent pool of trusted pen testers. Their SaaS platform delivers …
Pentera is the category leader for Automated Security Validation, allowing every organization to test with ease the integrity of all cybersecurity layers, unfolding true, current security exposures at any moment, …
Founded by mathematicians and cyber defense experts in 2013, Darktrace is a global leader in cybersecurity AI, delivering complete AI-powered solutions in its mission to free the world of cyber …
HackerOne is a SaaS platform that enables security researchers to find and report security holes to companies before they can get exploited. More than 400 companies, including Adobe, Yahoo, Twitter, …
Aikido is the get-it-done security platform for developers. Aikido centralizes all necessary code and cloud security scanners in one place. Created by pragmatic engineers, they put open-source solutions and the …
NetSPI delivers application and network security solutions to enterprise organizations, globally. Their security testing experts and proprietary technology platform empower organizations to scale and operationalize their security testing programs. Contact …
XM Cyber is the global leader in cyber attack path management The XM Cyber platform enables companies to rapidly respond to cyber risks affecting their business-sensitive systems by continuously finding …
Chainstack provides unified access to multichain node and data APIs, distributed compute and storage, identity management and security testing, and the ever-expanding list of services and tools to build amazing …
SafeBreach is a cybersecurity company based in Sunnyvale, California and Tel Aviv, Israel. The company has developed a platform that simulates hacker breach methods, running continuous "war games" to identify …
Astra is a cybersecurity SaaS company headquartered in the U.S. and India. Astra is the trusted security partner for leading brand including Muthoot Finance, Loom, CompTIA, NIIT, Goldcast, HackerRank, Sprinto, …
Picus Security, the leading security validation company, gives organizations a clear picture of their cyber risk based on business context. Picus transforms security practices by correlating, prioritizing, and validating exposures …
Scrut Automation is on a mission to simplify risk-based information security for cloud-native companies. Security can be complicated. With hundreds of assets and controls to monitor, an increasing number of …
Adversarial Exposure Validation (AEV) is a technique used mainly in machine learning and data science to detect dataset shift or data leakage between different datasets (usually training vs. validation/test datasets). It helps determine whether the model might perform unrealistically well because the datasets are not distributed the same way.
AEV trains a secondary model whose job is to answer one question:
“Can a model tell whether a sample comes from dataset A or dataset B?”
For example:
Dataset A → Training data
Dataset B → Validation/Test data
You label them like this:
| Data Source | Label |
|---|---|
| Training dataset | 0 |
| Validation/Test dataset | 1 |
Then you train a classifier to distinguish them.
Combine datasets
Merge training and validation/test data.
Add source labels
Training samples → label 0
Validation/test samples → label 1
Train a classifier
Any classifier works (e.g., logistic regression, random forest, gradient boosting).
Measure performance
Evaluate using metrics like AUC.
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