• G.Sumanth
• P. Mahesh Kumar
• Dr. K. Kranthi Kumar
• P. Poojitha
• G.S.L.Sowmya

Android platform due to open source characteristic and android is the largest share in the global market. Being the world's most popular operating system, it has drawn the attention of cyber criminals operating particularly through wide distribution of malicious applications. In this project we propose an effective machine-learning based approach for Android Harmful Applications Detection making use of evolutionary Genetic algorithm for discriminatory feature selection. Selected features from Genetic algorithm are used to train machine learning classifiers SVM(Support Vector Machine), ANN(Artificial Neural Network), Gaussian Naive Bayes , Random Forest, Decision Tree, XGBoost and their capability in identification of Malware before and after feature selection is compared. The experimentation results validate that Genetic algorithm gives most optimized feature subset helping in reduction of feature dimension to less than half of the original feature-set.

"Android Harmful Applications Detection Using Genetic Algorithm and Machine Learning Algorithms ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.l400-l406, May-2023, Available :http://www.jetir.org/papers/JETIR2305B60.pdf

"Android Harmful Applications Detection Using Genetic Algorithm and Machine Learning Algorithms ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppl400-l406, May-2023, Available at : http://www.jetir.org/papers/JETIR2305B60.pdf