Assessment of Machine Learning Classifiers for Malware Detection

• Published in: International journal of recent technology and engineering Vol. 8; no. 5; pp. 1840 - 1844
• Main Authors: Meghana, K., Priya, K.Satya, Sruthi, T.V.V.L., Gunasekhar, T.
• Format: Journal Article
• Language: English
• Published: 30.01.2020
• ISSN: 2277-3878; 2277-3878
• DOI: 10.35940/ijrte.E4940.018520

In our daily life, cell phones (e.g., cell phones and tablets) have met an expanding business achievement and have turned into an essential component of the regular daily existence for billions of individuals all around the globe. Day by day the advancements in technology is growing like an infinity thing .And the advancements in technology made everyone to use the smart phones and tablets regardless their professions .Everyday a big range of apps coming in to existence which made our lives very comfortable. While installing these apps without knowing we are allowing some malware in to our mobile which may leads to leakage of once private information. So in this paper we are going to analyze some machine learning techniques which will help in malware classification by taking the dataset. In this paper we calculated accuracy rate of malware classifiers such as KNN, Random Forest, SVM, and Gaussian Etc. Where we will be rating all these machine learning techniques according to their rate of accuracy. According to the experiments what we conducted Random forest stood as the best malware classifiers among all the other classifiers. We accept our study will be a reference work for specialists and experts in this examination field.