Search Results for author:
Found 9 papers, 3 papers with code
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of fake components, e. g., permissions and system calls, without compromising intrusive functionality.
PowerDrive: Accurate De-Obfuscation and Analysis of PowerShell Malware
We used PowerDrive to successfully analyze thousands of PowerShell attacks extracted from various malware vectors and executables.
Cryptography and Security
On the Effectiveness of System API-Related Information for Android Ransomware Detection
The attained results showed that systems based on System API could detect ransomware and generic malware with very good accuracy, comparable to systems that employed more complex information.
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files.
Cryptography and Security
Explaining Black-box Android Malware Detection
In this work, we generalize this approach to any black-box machine- learning model, by leveraging a gradient-based approach to identify the most influential local features.
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.
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
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.
