A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks

Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning

(2021) Shruti Patil et al.   Algorithms

Applications in Security and Evasions in Machine Learning: A Survey

(2020) Ramani Sagar et al.   Electronics

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

(2020) Kamran Shaukat et al.   Energies

Boosting algorithms for network intrusion detection: A comparative evaluation of Real AdaBoost, Gentle AdaBoost and Modest AdaBoost

(2020) Amin Shahraki et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

SECUR-AMA: Active Malware Analysis Based on Monte Carlo Tree Search for Android Systems

(2019) Riccardo Sartea et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

(2018) Battista Biggio et al.   PATTERN RECOGNITION

A Survey of Adversarial Machine Learning in Cyber Warfare

(2018) Vasisht Duddu   DEFENCE SCIENCE JOURNAL

MAAR: Robust features to detect malicious activity based on API calls, their arguments and return values

(2017) Zahra Salehi et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Impact of Network Structure on Malware Propagation: A Growth Curve Perspective

(2016) Hong Guo et al.   JOURNAL OF MANAGEMENT INFORMATION SYSTEMS

Adversarial Feature Selection Against Evasion Attacks

(2016) Fei Zhang et al.   IEEE Transactions on Cybernetics