3 papers
cs.CR2026
On the Reliability and Stability of Selective Methods in Malware Classification Tasks
Alexander Herzog, Aliai Eusebi, Lorenzo Cavallaro
The performance figures of modern drift-adaptive malware classifiers appear promising, but does this translate to genuine operational reliability? The standard evaluation paradigm…
cs.CR2025
On the Security Risks of ML-based Malware Detection Systems: A Survey
Ping He, Yuhao Mao, Changjiang Li +3
Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed. Howev…
cs.CR2025
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Ping He, Lorenzo Cavallaro, Shouling Ji
Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detec…