ABSTRACT

Malware is one of the major issues regarding the operating system or in the software world. The android system is also going through the same problems. We have seen other Signature-based malware detection techniques were used to detect malware. But the techniques were not able to detect unknown malware. Despite numerous detection and analysis techniques are there, the detection accuracy of new malware is still a crucial issue. In this paper, we study and highlight the existing detection and analysis methods used for the android malicious code. Along with studying, we propose Machine learning algorithms that will be used to analyze such malware and also we will be doing semantic analysis. We will be having a data set of permissions for malicious applications. Which will be compared with the permissions extracted from the application which we want to analyze. In the end, the user will be able to see how much malicious permission is there in the application and also we analyze the application through comments.

EXISITNG SYSTEM

In the existing system, the application permissions are extracted to detect the malware and executed through the command prompt. A proper GUI was not provided to execute the tasks. All the commands were run through the command prompt. It was difficult for the non-technical user to use the system. And also Semantic analysis was not implemented.

PROPOSED SYSTEM

In the proposed system, we are doing the permission-based analysis and also the semantic analysis. The permission-based analysis is been done on the web-based UI while the existing systems were just doing it all on the local machine in the command prompt. In our system, we have implemented an admin panel as well as a user panel. In the admin panel admin have the access to upload the apk files and its details along with its categorization and also the admin can upload the comment that can be used for semantic analysis.

In the user-panel the user can see the select the category of the application and can see its details like pricing description name. User can see the malicious percentage of the application. And the processed output of the semantic analysis will be displayed to the user in the form of graph and the user will get a proper review of the application.

SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10

HARDWARE REQUIREMENTS:

 Processor : Core I3
 RAM Capacity : 2 GB
 Hard Disk : 250 GB
 Monitor : 15″ Color
 Mouse : 2 or 3 Button Mouse
 Key Board : Windows 08/10