GitHub - Sopuru/AndroidMalDetection: Although several models have been developed for detecting and categorizing malicious Androidapplications, most network-based frameworksutilize long lists of network features to achieve an average classification accuracy (85.09%), and precision (89.10%). Our proposed model streamlines these lists to seven network flow-based features and achieved an average classification accuracy of (93.62%), success rate (92.68%), and a false positive (0.083). Experiments were carried out to evaluate the performance of three machine learning algorithms (Naive Bayes, J48 and, Random Forest) thereby identifying the best learner(s). Different sizes of training data were also considered for different experiments in other to evaluate learning rates based on different data size. At the end of our experiments, we identified seven top network flow-based features that can be used to effectively detect and categorize android malware. Keywords: Networkflow-based features, machine learning, Android malware, malware detection and classification, malware family

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Although several models have been developed for detecting and categorizing malicious Androidapplications, most network-based frameworksutilize long lists of network features to achieve an average classification accuracy (85.09%), and precision (89.10%). Our proposed model streamlines these lists to seven network flow-based features and achieved …

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
.idea		.idea
experiments		experiments
serving_static/templates		serving_static/templates
static		static
templates		templates
venv		venv
.gcloudignore		.gcloudignore
Deployment.py		Deployment.py
EI.ipynb		EI.ipynb
RNNFINAL4_model.sav		RNNFINAL4_model.sav
app.yaml		app.yaml
requirements.txt		requirements.txt