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Zero false positives

Expert intelligence + effective automation

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Vulnerability classifiers

A pipeline to classify vulnerable code

After our first encounter with the Python machine learning ecosystem, we are ready for a first attempt at creating a vulnerability classifier from samples of labeled code. Our objective is to...

Data and regression line, crop.

Digression to Regression

Simple linear regression in scikit

After our risk quantification series, it should come as no surprise that we like numbers and measuring. However, when confronted with the question: "Why do you need to translate code into...

Syringe ready to inject bad stuff. Credit: https://pixabay.com/es/photos/jeringa-healthcare-aguja-medicina-417786/

Tainted love

It's all about sanitization

In the several past articles, we have briefly touched on the concept of taint analysis. In this article, we would like to fill in the gaps which maybe have been raised by these careless...

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Fool the machine

Trick neural network classifiers

Artificial Neural Networks (ANNs) are certainly a wondrous achievement. They solve classification and other learning tasks with great accuracy. However, they are not flawless and might misclassify...

Git. Photo by Yancy Min on Unsplash: https://unsplash.com/photos/842ofHC6MaI/

Big Code

Learning from open source

In our Machine Learning (ML) for secure code series the mantra has always been the same: to figure out how to leverage the power of ML to detect security vulnerabilities in source code, regardless...

Photo by Andres Urena on Unsplash. Credits: https://unsplash.com/photos/k1osF_h2fzA

Natural code

Natural language processing for code security

Our return to the Machine Learning (ML) for secure code series is a bit of a digression, but one too interesting to resist. At the same time, it is not, since the Natural Language Processing (NLP)...

Choices. Photo by Nathan Dumlao on Unsplash: https://unsplash.com/photos/pMW4jzELQCw

Risk indicator roundup

A matter of taste

What is the best risk indicator? Bottom line: there is no "best", only different approaches to the same thing. Ultimately, it’s up to you. Here we will show the pros and cons of each so you can...

Parsing code. Photo by Markus Spiske on Unsplash: https://unsplash.com/photos/hvSr_CVecVI

Parse and Conquer

Why Asserts uses Parser combinators

As you might have noticed, at Fluid Attacks we like parser combinators, functional programming, and, of course, Python. In the parser article, I showed you the essentials of Pyparsing and we also...

Chess strategy. Photo by Inactive. on Unsplash: https://unsplash.com/photos/nAjil1z3eLk

Great Expectations

What to expect when you're at risk

Thus far, the situations we have modeled have been either over-simplifications or fabrications in order to illustrate a concept. This article will try to improve on that a bit by considering more...

Finance simulation. Photo by M. B. M. on Unsplash: https://unsplash.com/photos/ZzOa5G8hSPI

Quantitative Python

Risk management with Python

Now that we have an understanding of risk concepts such as the loss exceedance curve, value-at-risk, Bayes Rule, and fitting distributions, we would like to have a realiable, extensible and...

Fire extinguisher. Photo by Tommaso Pecchioli on Unsplash: https://unsplash.com/photos/XG_wi3W4-m8

Para bellum

Prepare for the worst risk

"Si vis pacem, para bellum", goes the old adage. If you want peace, prepare for war. In our case, the worst possible risky scenario our information assets could go into. While probability...

Baseball hit. Photo by Chris Chow on Unsplash: https://unsplash.com/photos/BhwRQr08PcM

Hit or miss

Estimating attack probability

One of the main obstacles against adopting a quantitative approach to risk management is that since major security breaches are relatively rare and hence, there cannot be enough data for proper...

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