LEVERAGING CO-EXISTENCE FEATURES TO IMPROVE ANDROID MALWARE DETECTION

LEVERAGING CO-EXISTENCE FEATURES TO IMPROVE ANDROID MALWARE DETECTION
Author(s):
DONGARI SAI, Dr.K.Santhi Sree
Keywords:
co-existence, FP-growth, machine learning, malware.
Abstract
This project examines Android malware detection using datasets such as Drebin, Malgenome, and CIC_MALDROID2020, which offer extensive API and Permission data for in-depth analysis. We employed several machine learning models for classification, including Logistic Regression, SVM, KNN, Random Forest, Decision Tree, and a Stacking Classifier that blends Random Forest, MLP, and LightGBM. The comprehensive methodology involves data preprocessing, model training, and performance evaluation to create highly effective detection models. These models enhance mobile security by improving malware threat detection and mitigation. Our findings are particularly valuable for professionals and researchers in mobile security. Additionally, we developed a user-friendly Flask framework with SQLite, enabling secure signup, sign-in, and user testing, thereby making the project more practical and robust for efficient user interactions.
Article Details
Unique Paper ID: 167376 Publication Volume & Issue: Volume 11, Issue 3 Page(s): 903 - 906
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