The Analysis of Feature Selection Methods and Classification Algorithms in Permission Based Android Malware Detection

Date

2014-12-12

Author

Pehlivan, Ugur
Baltaci, Nuray
Acartürk, Cengiz
Baykal, Nazife

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

158
views

0
downloads

Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software. In the present study, we analyze two major aspects of permission-based malware detection in Android applications: Feature selection methods and classification algorithms. Within the framework of the assumptions specified for the analysis and the data used for the analysis, our findings reveal a higher performance for the Random Forest and J48 decision tree classification algorithms for most of the selected feature selection methods.

Subject Keywords

Malware detection, Classification, Feature selection, Static analysis, Machine learning, Android application, Cyber security

URI

https://hdl.handle.net/11511/54345

Collections

Graduate School of Informatics, Conference / Seminar

Suggestions

OpenMETU
Core

AntiWare: An Automated Android Malware Detection Tool based on Machine Learning Approach and Official Market Metadata Akhuseyinoglu, Nuray Baltaci; Akhuseyinoglu, Kamil (2016-10-22) The prevalence of mobile devices has increased rapidly in recent years. People store valuable data like personal and financial information on those devices. In addition, applying "bring your own device (BYOD)" policy in companies has become popular. Hence, mobile devices are also source of valuable and confidential company information. Accordingly, there is a growing need for malware detection methods and tools to protection mobile devices against attacks targeting them. In this study, an automated feature-...
A Comparative study on automated android application testing tools Hökelekli, Gülçin; Betin Can, Aysu; Department of Information Systems (2016) Nowadays, as mobile devices have become widespread, mobile application development has become an area which is considerably popular. This popularity increases the importance of mobile application testing. Distinguishing properties of mobile devices increase the importance of test automation. Thus, the number of mobile test automation tools is growing. Each tool has some advantages and limitations. The aim of this study is to compare the most popular mobile testing tools. We choose Android testing tools beca...
A novel user activity prediction model for context aware computing systems Peker, Serhat; Koçyiğit, Altan; Department of Information Systems (2011) In the last decade, with the extensive use of mobile electronic and wireless communication devices, there is a growing need for context aware applications and many pervasive computing applications have become integral parts of our daily lives. Context aware recommender systems are one of the popular ones in this area. Such systems surround the users and integrate with the environment; hence, they are aware of the users' context and use that information to deliver personalized recommendations about everyday ...
An Exploratory Study on the Outcomes of Influence Strategiesin Mobile Application Recommendations Ünal, Perin; Taşkaya Temizel, Tuğba; Eren, Pekin Erhan (null; 2014-05-23) The rapid growth in the mobile application market presents a significant challenge to find interesting and relevant applications for users. Recommendation systems deal with ends such as movies and consumer goods that are consumed by users where similarity between consumer tastes is generally taken into account. On the other hand, recommendation systems for mobile applications differ from traditional systems in terms of the characteristics of the ends they recommend. They present applications that are not ju...
An Efficient graph-theoretical approach for interactive mobile image and video segmentation Şener, Ozan; Alatan, Abdullah Aydın; Department of Electrical and Electronics Engineering (2013) Over the past few years, processing of visual information by mobile devices getting more affordable due to the advances in mobile technologies. Efficient and accurate segmentation of objects from an image or video leads many interesting multimedia applications. In this study, we address interactive image and video segmentation on mobile devices. We first propose a novel interaction methodology leading better satisfaction based on subjective user evaluation. Due to small screens of the mobile devices, error ...

Citation Formats

U. Pehlivan, N. Baltaci, C. Acartürk, and N. Baykal, “The Analysis of Feature Selection Methods and Classification Algorithms in Permission Based Android Malware Detection,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54345.