GEAAD: generating evasive adversarial attacks against android malware defense.

Area of Science:

• Cybersecurity
• Machine Learning
• Mobile Security

Background:

• Android's dominance makes it a target for cyberattacks.
• Malware detection systems are crucial for combating evolving threats.
• Adversarial examples are increasingly used to evade detection.

Purpose of the Study:

• To investigate the effectiveness of Generative Adversarial Networks (GANs) in Android malware detection.
• To analyze the impact of adversarial examples, specifically from a dual-opponent generative adversarial network (DOpGAN), on malware detection systems.
• To propose strategies for enhancing Android security against sophisticated evasion techniques.

Main Methods:

• Utilized static and dynamic analysis with machine learning, focusing on GANs.
• Implemented the Android Opcode Modification GAN with the Opcode Frequency Optimal Adjustment algorithm.
• Introduced and analyzed the dual-opponent generative adversarial network (DOpGAN) for generating adversarial examples.

Main Results:

• The Android Opcode Modification GAN improved malware detection by modifying opcode distribution.
• The DOpGAN demonstrated a grey-box attack strategy, successfully misclassifying malware as benign and evading detection.
• Adversarial examples generated by DOpGAN highlight significant challenges for current detection systems.

Conclusions:

• Adversarial examples generated by DOpGAN pose a substantial threat to Android security.
• There is a critical need to integrate adversarial example detection systems into the Android security framework.
• Continuous innovation and collaboration are essential for resilient Android security against emerging threats.