Trisentry-MD- A unified and high performance deep ensemble model for android malware detection

Modern Android malware changes rapidly, making it hard for traditional anti-malware methods to identify these new, disguised and flexible threats. Because existing solutions have issues with many false positives, difficulty handling several types of malware and slow reaction times, hence the work introduce TriSentry-MD as a novel framework for detecting malware in an efficient manner in real time with greater more accurately. The proposed work main breakthrough is achieved by incorporating three different yet matching techniques: GrayCodeNet, to image the resultant bytecode and examine it with convolutional neural networks and a novel BehavWatch-X, which perform normalization and aggregate execution into behaviour feature vector that monitor run time metrics includes CPU usage, battery consumption, network activity, and system calls. Along with proposed ReVersaLearn, a technique for reverse-engineering and using app metadata in classification. Thus, it starts with getting an APK, dividing it into static analysis, bytecode analysis and testing its behaviour in a virtual sandbox. The implementation outputs are gathered and turned into a final outcome by the soft voting process. With harmless attacks from Drebin and AMD, TriSentry-MD obtained near-perfect detection accuracy and almost no false alarms. The findings demonstrate that using Manuscript Click here to access/download;Manuscript;Manuscript.docx multiple data types in a unified framework is effective for discovering a wider variety of android threats and making threat detection more accurate and stronger.