Android malware detection by machine learning apprehension and static feature characterization

The increased usage and popularity of Android devices encourage malware developers to generate newer ways to launch malware in different packaged forms in different applications. These malware causes various information leakage and money lost. For example, only in Canada, McAfee, which surveyed 1,000 Canadians and found 65% of them, had lost more than $100 and almost a third had lost more than $500 to various cyber scams so far this year. Moreover, after identifying software as malware, unethical developer repackages the detected one and again launches the software. Unfortunately, repackaged software remains undetected mostly. In this research three different tasks were done. Comparing to the existing work we have used source code based analysis using bag-of words algorithm in machine learning. By modifying Bag-of-word procedure and adding some additional preprocessing of dataset the evaluation results represent 0.55% better than the existing work in this field. In that case re-packaging was included and this is a new edition in this field of research. Moreover in this research, a vocabulary was also created to identify the malicious code. Here with existing 69 malicious patterns more 12 malicious patterns were added. In addition to these two contributions, we have also implemented our model in a web application to test. This paper represents such a model, which will help the developers or antivirus launcher to detect malware if it is repackaged. This vocabulary will also help to do so.