Comparison of classification algorithms for mobile malware detection: market metadata as input source

Download

index.pdf

Date

2014

Author

Baltacı, Nuray

Metadata

Show full item record

Item Usage Stats

224
views

388
downloads

The prevalence of mobile devices has been catching the attention of malware authors especially for Android OS supported devices due to its user-centric security policy and open application development strategy for its official application market. In this study, an automated feature-based static analysis method was applied to detect malicious mobile applications on Android devices. The main purpose of the study is to investigate the contribution of other application market metadata to the detection of malicious applications in addition to requested permissions. Hence, the information of applications presented on the official market when a user wants to download them was used as the feature set for training supervised classification algorithms. This feature set includes permissions requested from the user at the installation time, and other metadata about an application including but not limited to application category, download number category, and developer name. Additionally, different classification algorithms were compared in terms of their predictive accuracy and the effect of feature selection algorithms on the improvement of classification task was investigated. Naïve Bayes, k-nearest neighbor, J48 and random forest were chosen as classification algorithms. As filter-based algorithms, Chi-Square, Information Gain and ReliefF feature selection methods were utilized to reduce the number of attributes used to train those classification algorithms.

Subject Keywords

Malware (Computer software)., Computer security., Data protection., Mobile communication systems.

URI

http://etd.lib.metu.edu.tr/upload/12617716/index.pdf
https://hdl.handle.net/11511/23898

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

Permission based malware detection analysis in android applications Pehlivan, Uğur; Baykal, Nazife; Department of Information Systems (2014) Android mobile devices have developed very fast in past decade and have been very widespread in all over the world. Nowadays, several applications are available on application markets. The number of android applications also increases with the increase in the variety of applications. Those applications may become very dangerous for the users of android mobile devices because of fast development and wide variety of applications. Some applications may have the malicious activities such as novelty and amusemen...
Malicious code detection in android: the role of sequence characteristics and disassembling methods Gürkan Balıkçıoğlu, Pınar; Şırlancı, Melih; ACAR KÜÇÜK, ÖZGE; Ulukapi, Bulut; Turkmen, Ramazan K.; Acartürk, Cengiz (2022-11-01) The acceptance and widespread use of the Android operating system drew the attention of both legitimate developers and malware authors, which resulted in a significant number of benign and malicious applications available on various online markets. Since the signature-based methods fall short for detecting malicious software effectively considering the vast number of applications, machine learning techniques in this field have also become widespread. In this context, stating the acquired accuracy values in ...
A Study on User Perception of Mobile Commerce for Android and iOS Device Users ÜNAL, PERİN; Taşkaya Temizel, Tuğba; Eren, Pekin Erhan (2015-08-26) ustomer profiling in the mobile commerce (m-commerce) domain has recently gained importance due to the increased proliferation of smartphones and tablets. One of the major challenges confronting m-commerce developers is the need to know user perceptions of m-commerce applications in order to better design and deliver m-commerce services. In this paper, user perceptions of mobile commerce applications is analyzed based on their gender and the operating system (OS) of the devices in use, which are important f...
Performance evaluation of lightweight cryptographic algorithms for internet of things security Polat, Selahattin; Baykal, Nazife; Department of Cyber Security (2019) Widespread deployment of mobile and embedded devices in everyday use has brought up not only new concepts and application areas such as Internet-of-Things (IoT) but also several security and privacy problems. In theory, it is possible to mitigate most of these problems by implementing well-known and standardized security algorithms and techniques on IoT devices. However, in practice, it is rather difficult, if not impossible, to implement standard security algorithms on these devices due to their limited re...
Clustering and Visualization of Mobile Application Permissions for End Users and Malware Analysts Canbek, Gurol; Baykal, Nazife; SAĞIROĞLU, Şeref (2017-04-28) Application permissions at the core of Android security mechanism are the first leading transparent feature for users to assess any mobile application before download or installation and for experts to analyse any malware. Representing vast, dispersed permissions and achieving clarity is not a trivial matter. In this study, we first examined Android permissions, their groups and formal representations with the limitations. We also surveyed limited studies on clustering/visualization of permissions. We group...

Citation Formats

N. Baltacı, “Comparison of classification algorithms for mobile malware detection: market metadata as input source,” M.S. - Master of Science, Middle East Technical University, 2014.