Another year, another edition of the Machine Learning Security Evasion Competition (MLSEC) took place! If you don’t know what we are talking about, it is a competition promoted by some well-known Internet companies (CUJO AI, Microsoft, MRG Effitas, VMRay, and Nvidia) in which one has (i) to develop machine learning-based Read more…
If you read any of the SECRET papers, it is not a SECRET for you that using Machine Learning (ML) to detect malware is a challenging endeavor. In one of these papers, we presented the results of our participation in a machine learning-based malware detection evasion challenge dating back 2019. Read more…
Last year we were breaking machine learning models for malware detection. We were able to bypass all the models of the competition, finishing in the second position — unfortunately, it was not enough to win an NVIDIA graphic card, but it was worth it anyway, after all, the competition winner Read more…
We are happy to share with you our newest machine learning secret! Scikit-multiflow 0.5 is now available, including our open-source contribution: delayed labels evaluation for supervised learning. We expect that the community makes great use of this new feature, which is very important for cybersecurity solutions. Check out the new Read more…
Last month SECRET started a collaboration with researchers from the University of Waikato, located in Hamilton, New Zealand. The University of Waikato is famous for developing open-source machine learning frameworks, such as Weka, MOA, and scikit-multiflow. The main objective of this collaboration is to develop novel machine learning solutions that Read more…
Applying machine learning techniques to cyber security has become popular nowadays. The majority of existing work aim to produce results that achieve accuracy, f1 score and precision rates close to 100%, without taking into account problems such as adversarial samples. In our work, “Shallow Security: on the Creation of Adversarial Read more…
Tamy Beppler, a SECRET researcher, has been presenting her research in the 2019 Information Security Conference. Tamy investigated the use of file textutres for malware families’ classification. Interested in taking a look on her results? Check this link.
An Internet company has been promoting a competition that challenges researchers to bypass machine learning models for malware detection. SECRET has been participating this challenge and our members (Fabrício Ceschin and Marcus Botacin) were able to bypass all models! Want to know more about how to code a binary that Read more…
SBSEG proceeded with a great presentation of Fabricio Ceschin, a SECRET member, about the use of machine learning techniques for security purposes. Fabricio discussed common challenges and pitfalls when designing ML experiments. Check all details on github.
Among many initiatives, Google awards research work in promising fields developed in Latin America. Fabricio’s work was awarded in the machine learning category and proposed detecting concept drift in malware classifier. Concept drift is the name of the phenomenon which causes classifier’s accuracy to be reduced due to changes in Read more…