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Evasion Attacks against Machine Learning at Test Time
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data.
AdversariaLib: An Open-source Library for the Security Evaluation of Machine Learning Algorithms Under Attack
We present AdversariaLib, an open-source python library for the security evaluation of machine learning (ML) against carefully-targeted attacks.
Security Evaluation of Support Vector Machines in Adversarial Environments
Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering.
Poisoning Behavioral Malware Clustering
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems.
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection.
Cryptography and Security
