MalPurifier: Enhancing Android Malware Detection With Adversarial Purification Against Evasion Attacks

Machine learning systems used to detect Android malware (malicious software on Android phones) are vulnerable to evasion attacks, where attackers modify malware to trick the detection system into missing it. Researchers developed MalPurifier, a defensive framework that uses adversarial purification (a technique that removes deceptive modifications from suspicious code) combined with a Denoising AutoEncoder (a type of neural network that learns to clean up noisy or corrupted data) to protect detection systems and maintain accuracy above 90% against various evasion attacks.