Enhanced Android Malware Detection: An SVM-Based Machine Learning Approach

The cybersecurity of increasing numbers of mobile devices and their users are threatened by malicious applications. Detecting malicious Android applications is a challenge due to the massive number of Android applications and their various properties which provide a large set of features and a spars...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:International Conference on Big Data and Smart Computing S. 75 - 81
Hauptverfasser: Han, Hyoil, Lim, SeungJin, Suh, Kyoungwon, Park, Seonghyun, Cho, Seong-je, Park, Minkyu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.02.2020
Schlagworte:
Android Malware Detection
Androids
API Calls
Feature extraction
Humanoid robots
Machine learning
Malware
Static analysis
Support vector machines
ISSN:2375-9356
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:The cybersecurity of increasing numbers of mobile devices and their users are threatened by malicious applications. Detecting malicious Android applications is a challenge due to the massive number of Android applications and their various properties which provide a large set of features and a sparse dataset. We focus on the resources the Android applications call and employ the Application Program Interface (API) calls as features. The dataset used in this work is from an Android environment where malicious and benign applications frequently access the system resources through Android API calls. Since an Android application would invoke a relatively small number of APIs in ordinary scenarios, data in the dataset is inherently sparse and high dimensional. We experimented intensively with 58,602 Android applications as well as 133,227 features (i.e., API Calls). This paper presents a machine-learning-based approach using Support Vector Machines (SVM) to detect malicious Android applications; the new approach delivers results highly competitive with existing approaches.
ISSN:2375-9356
DOI:10.1109/BigComp48618.2020.00-96