Autonomous Android Malware Detection System Based on Static Analysis

Security measures for Android-powered devices are necessary to prevent sophisticated and malicious assaults. This entails creating automated systems capable of anticipating, identifying, and removing dangerous Android apps.Static analysis and feature engineering are used to train Long Short-Term Memory networks for malware detection once Android programs have been extracted.The information is somewhat outdated and less than 80% of the facts are true. To address these issues, the initiative offers a novel technique for Android malware detection and classification and uses static analysis to recover pertinent data. To help machine learning algorithms understand different malware types, specialized datasets are collected and standardized into a common format.The Long Short-Term Memory technique is used to recognize and classify Android malware. Two more suggestions are made by the study, which analyzes data using a functional API deep learning model. To develop accurate and targeted detection algorithms, researchers collect specialized datasets for several types of malware (such as ransomware, scarware, adware, and SMS malware). Features that can be created from raw malware data include opcode sequences, Application Programming Interface calls, and n-grams. These features can also be obtained from code or behavioral logs. Using scaling and normalizing to make sure the extracted features have comparable ranges and magnitudes improves the performance of machine learning models.Accurate and effective malware detection models are created by applying static analysis techniques such as disassembling the Android Package Kit and inspecting the byte code for known malware signatures.