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Found 7 papers, 2 papers with code
Online Clustering of Known and Emerging Malware Families
Clustering algorithms are thus becoming more widely used in computer security to analyze the behavior of malware variants and discover new malware families.
A Comparison of Adversarial Learning Techniques for Malware Detection
Machine learning has proven to be a useful tool for automated malware detection, but machine learning models have also been shown to be vulnerable to adversarial attacks.
Keystroke Dynamics for User Identification
In previous research, keystroke dynamics has shown promise for user authentication, based on both fixed-text and free-text data.
Creating Valid Adversarial Examples of Malware
Machine learning is becoming increasingly popular as a go-to approach for many tasks due to its world-class results.
Classification and Online Clustering of Zero-Day Malware
Based on the classification score of the multilayer perceptron, we determined which samples would be classified and which would be clustered into new malware families.
Combining Generators of Adversarial Malware Examples to Increase Evasion Rate
Antivirus developers are increasingly embracing machine learning as a key component of malware defense.
Parallel Instance Filtering for Malware Detection
The main idea of the algorithm is to split the data set into non-overlapping subsets of instances covering the whole data set and apply a filtering process for each subset.
