Andorid Malware Detection APK File in Python Projects

Andorid Malware Detection APK File in Python Projects

Abstract
With the rapid growth of mobile applications, Android has become the most widely used operating system, making it a primary target for cybercriminals. Malicious Android apps (malware) embedded in APK files can steal sensitive data, track user activity, or damage devices. Traditional antivirus tools rely heavily on signature-based detection, which fails against new and evolving malware. This project, Android Malware Detection from APK Files in Python, aims to develop a machine learning–based system to detect malware by analyzing APK files. Features are extracted from APKs such as permissions, API calls, and metadata, and then classified using machine learning/deep learning models. Python libraries such as Androguard, Scikit-learn, and TensorFlow/Keras are used to build the detection pipeline. The system helps in automatically identifying harmful APKs and protecting users against mobile security threats.

Existing System
Existing Android malware detection systems primarily depend on signature-based or heuristic analysis, which compares APK files against known malware databases. While effective for previously identified threats, these methods fail to detect zero-day malware or polymorphic variants that modify their code to evade detection. Some advanced systems use sandboxing to execute APKs in a virtual environment and monitor behavior, but this approach is computationally expensive and not scalable for large-scale detection. Additionally, many antivirus apps for mobile devices lack intelligent learning capabilities, which results in false positives and missed threats.

Proposed System

The proposed system introduces a Python-based Android malware detection model that leverages static and dynamic analysis combined with machine learning. APK files are first decompiled, and relevant features such as requested permissions, intent filters, API calls, and manifest details are extracted using tools like Androguard. These features are then transformed into numerical representations for machine learning classifiers such as Random Forest, SVM, Gradient Boosting, or deep learning models like CNNs and LSTMs. The model is trained on datasets of benign and malicious APKs to classify new apps as safe or malicious. Compared to traditional methods, this approach provides better accuracy, adaptability to new malware variants, and scalability. A user-friendly interface can be developed to allow users to upload APK files and instantly receive a security report, making the system practical for researchers, developers, and end-users.

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