Assessment of Machine Learning Classifiers for Malware Detection
K. Meghana1, K. Satya Priya2, T. V. V. L. Sruthi3, T. Gunasekhar4
1K.Meghana, Department, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation situated at Vaddeswaram, Guntur District.
2K.Satya Priya, Department, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation situated at Vaddeswaram, Guntur District.
3T.V.V.L.Sruthi, Department, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation situated at Vaddeswaram, Guntur District.
4T. Gunasekhar, Associate Professor, Department, Computer Science and Engineering, Koneru Lakshmaiah Educational Foundation situated at Vaddeswaram, Guntur District.
Manuscript received on January 02, 2020. | Revised Manuscript received on January 15, 2020. | Manuscript published on January 30, 2020. | PP: 1840-1844 | Volume-8 Issue-5, January 2020. | Retrieval Number: E4940018520/2020©BEIESP | DOI: 10.35940/ijrte.E4940.018520
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Mobiles And Pcs, Malware Detection, Classification Algorithms, Random Forest.
Scope of the Article: Classification.