%0 Journal Article %1 noauthororeditor %A Dhote, Mr. Tushar Patil | Prof. Bharti %D 2019 %J International Journal of Trend in Scientific Research and Development %K Bayes Calls Classifier Computers Density Detection Engineering Frequency Malware Naive System %N 5 %P 2401-2403 %R https://doi.org/10.31142/ijtsrd26449 %T Malware Detection in Android Applications %U https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil %V 3 %X Android is a Linux based operating system used for smart phone devices. Since 2008 Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper we proposed dynamic malware detection approach for Android applications. In dynamic analysis system calls are recorded to calculate the density of the system calls. For density calculation we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd) ISSN: 2456-6470 Volume-3 | Issue-5 August 2019 URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil

@article{noauthororeditor, abstract = {Android is a Linux based operating system used for smart phone devices. Since 2008 Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper we proposed dynamic malware detection approach for Android applications. In dynamic analysis system calls are recorded to calculate the density of the system calls. For density calculation we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd) ISSN: 2456-6470 Volume-3 | Issue-5 August 2019 URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil }, added-at = {2019-09-12T15:09:03.000+0200}, author = {Dhote, Mr. Tushar Patil | Prof. Bharti}, biburl = {https://www.bibsonomy.org/bibtex/28b5bd81d458b2caf6cee2205d759d6b6/ijtsrd}, doi = {https://doi.org/10.31142/ijtsrd26449}, interhash = {5a828d9309b01dc98126c9a0af0326ce}, intrahash = {8b5bd81d458b2caf6cee2205d759d6b6}, issn = {2456-6470}, journal = {International Journal of Trend in Scientific Research and Development}, keywords = {Bayes Calls Classifier Computers Density Detection Engineering Frequency Malware Naive System}, language = {English}, month = aug, number = 5, pages = {2401-2403}, timestamp = {2019-09-12T15:09:03.000+0200}, title = {Malware Detection in Android Applications }, url = {https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil}, volume = 3, year = 2019 }