Malware Detection Using Machine Learning and Deep Learning

ISSN: 2321 9653; IC Value: 45.98; SJ Impact Factor: 7.538

Volume 10 Issue XI Nov 2022 Available at www.ijraset.com

Malware Detection Using Machine Learning and Deep Learning

Prof. Sharda Thete1, Siddheshwar Midgule2 , Nikesh Konde3 , Suraj Kale4 Alard College of Engineering & Management (ALARD Knowledge Park, Survey No. 50, Marunji, Near Rajiv Gandhi IT Park, Hinjewadi, Pune 411057)Approved by AICTE. Recognized by DTE. NAAC Accredited. Affiliated to SPPU(Pune University).

Abstract: Android application security is based on permission based mechanisms that restrict third party Android applications' access to critical resources on an Android device. The user must accept a set of permissions required by the application before proceeding with the installation. This process is intended to inform users about the risks of installing and using applications on their devices. However, most of the time, even with a well understood permission system, users are not fully aware of endangered threats, relying on application stores or the popularity of applications and relying on developers to You are accepting the install without trying to analyze your intent.

Keywords: ((Android applications, malware detection, permission related APIs, random forests, software security.) …

I. INTRODUCTION

Permissions are the foundation of the Android security concept. Permission is a security feature that restricts access to a portion of the code or data on the device. The restriction is in place to safeguard sensitive data and code from being abused to distort or harm the user experience.

Permissions are used to provide or deny access to APIs and resources that are restricted. The Android INTERNET permission, for example, is needed for applications to execute network communications; hence, the INTERNET permission restricts the establishing of a network connection.

A. Malware Detection

It is generally a program that is installed outside the user's will and can cause damage to both the operating system and the hardware (physical) elements of a computer.

1) Effects generated by the virus:

2) Destruction of files. changing the file size.

3) Delete all information on the disc, including formatting it.

Destruction of the file allocation table, which makes it impossible to read the information on the disk.

B. Normalization

A crucial component of the field of data management is data cleaning. A database's whole contents are reviewed as part of the data cleansing process, and any information that is missing, inaccurate, duplicated, or irrelevant is either updated or removed. Data cleansing involves finding a technique to optimize the dataset's accuracy without necessarily messing with the existing data. It does not just involve removing the old information to make room for new data. The process of identifying and fixing incorrect data is known as data cleaning. The majority of tasks performed by organizations rely on data, but few do so in a way that is effective. The most crucial phase in the data process is cleaning, categorization, and standardization of the data.

C. Feature Extraction

The larger the number of features, the easier the “dimension disaster” will occur, the more complex the model will become, and the greater the decrease in the model’s promotion ability will be. Feature selection can eliminate irrelevant or redundant features, thereby reducing the number of features, improving model accuracy, and reducing the runtime.

ISSN: 2321 9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue XI Nov 2022 Available at www.ijraset.com

D. System Structure

The malware detection techniques are proposed by the system. We used I the permission i) ranking based feature selection technique (ii) the similarity based permission feature selection(iii) the association rule mining technique (iv) the modified random forest classifier parameters) and (v) the modified random forest classifier parameters. The permission ranking based feature selection stratey and the permission feature selection technique based on similarity rate the characteristics based on frequency.

II. OBJECTIVES OF THE STUDY

To design developed an system for detect the malicious contents from third party API’s which is generally used for android application development. To implement a machine learning or deep learning algorithm to mine the API codes. To validate the entire API’s using background Knowledge which works like supervised learning approach.

III. SCOPE OF THE STUDY

Use case diagrams describe the high level functions and scope of a system. These diagrams also identify the interactions between the system and its actors. A Use case diagram outlines how external entities i.e. user interact with an internalsoftware system.

Diagram 1: Use Case Diagram

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

ISSN: 2321 9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue XI Nov 2022 Available at www.ijraset.com

A sequence diagram is a type of interaction diagram that shows how a process works. Others, in what order. This is the composition of the message sequence diagram. Sequence Diagram Shows object interactions in chronological order.

Diagram 2: Sequence Diagram

Activity diagrams are graphical representations of workflows of stepwise activities and actions with support for choice, iterationand concurrency.

Diagram 3: Activity Diagram

IV. CONCLUSIONS

Most of these Android devices use Android applications for communication and data sharing. The Android platform's permission mechanism restricted access to applications. Permissions can be used as elements of Android applications to detect malware.

V. ACKNOWLEDGEMENT

This paper was supported by Alard College of Engineering & Management, Pune 411057. We are very thankful to all those who have provided us valuable guidance towards the completion of this Seminar Report on “Malware Detection using Machine learning and Deep learning” as part of the syllabus of our course. We express our sincere gratitude towards the cooperative department who has provided us with valuable assistance and requirements for the system development. We are very grateful and want to express our thanks to Prof. Sharda Thete for guiding us in the right manner, correcting our doubts by giving us their time whenever we required, and providing their knowledge and experience in making this project.

Technology (IJRASET

ISSN: 2321 9653; IC Value: 45.98; SJ Impact Factor: 7.538 Volume 10 Issue XI Nov 2022 Available at www.ijraset.com

REFERENCES

[1] D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens, “DREBIN: Effective ¨ and Explainable Detection of Android Malware in Your Pocket,” 2014M. Young, The Technical Writer’s Handbook. Mill Valley,CA: University Science, 1989.

[2] R. Sato, D. Chiba, S. Goto, “Detecting Android Malware by Manifest File Parsing,” Proceedings Asia Pacific Advanced Network vol. 36, p. 23 31, 2013.K. Elissa, "Title of the work if known", unpublished.

[3] D. Arp, M. Spreitzenbarth, M. Hubner, H. Gascon, K. Rieck, and C. Siemens, “DREBIN: