Automated Android Malware Prediction

The exponential growth of Android applications has created unprecedented security challenges, with malicious software posing significant threats to mobile device users worldwide. Traditional signature-based detection methods prove inadequate against sophisticated malware variants that employ obfuscation and polymorphic techniques. This research introduces AMLDS (Automated Malware Learning Detection System), a novel framework that combines static analysis, dynamic behaviour monitoring, and deep learning algorithms to identify Android malware with exceptional accuracy. Our approach utilizes convolutional neural networks and ensemble learning methods to analyze application permissions, API calls, byte code patterns, and runtime behaviours. The system incorporates advanced feature extraction techniques including n-gram analysis, opcode frequency distribution, and network traffic pattern recognition.