A Novel Approach to Android Malware Intrusion Detection Using Zero-Shot Learning GANs

Abstract

This study proposes an innovative intrusion detection system for Android malware based on a zero-shot learning GAN approach. Our system achieved an accuracy of 99.99%, indicating that this approach can be highly effective for identifying intrusion events. The proposed approach is particularly valuable for analyzing complex datasets such as those involving Android malware. The results of this study demonstrate the potential of this method for improving the accuracy and efficiency of intrusion detection systems in real-world scenarios. Future work could involve exploring alternative feature selection techniques and evaluating the performance of other machine learning classifiers on larger datasets to further enhance the accuracy of intrusion detection systems. The study highlights the importance of adopting advanced machine learning techniques such as zero-shot learning GANs to enhance the effectiveness of intrusion detection systems in cybersecurity. The proposed system presents a significant contribution to the field of intrusion detection, providing an effective solution for detecting malicious activities in Android malware, which can improve the security of mobile devices.