Book ChapterDOI
Anomaly Detection using String Analysis for Android Malware Detection
Borja Sanz,Igor Santos,Xabier Ugarte-Pedrero,Carlos Laorden,Javier Nieves,Pablo García Bringas +5 more
- pp 469-478
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TLDR
A new method based on anomaly detection that extracts the strings contained in application files in order to detect malware is proposed.Abstract:
The usage of mobile phones has increased in our lives because they offer nearly the same functionality as a personal computer. Specifically, Android is one of the most widespread mobile operating systems. Indeed, its app store is one of the most visited and the number of applications available for this platform has also increased. However, as it happens with any popular service, it is prone to misuse, and the number of malware samples has increased dramatically in the last months. Thus, we propose a new method based on anomaly detection that extracts the strings contained in application files in order to detect malware.read more
Citations
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Journal ArticleDOI
A review on feature selection in mobile malware detection
TL;DR: This paper studied 100 research works published between 2010 and 2014 with the perspective of feature selection in mobile malware detection, and categorizes available features into four groups, namely, static features, dynamic features, hybrid features and applications metadata.
Journal ArticleDOI
A Taxonomy and Qualitative Comparison of Program Analysis Techniques for Security Assessment of Android Software
TL;DR: A comprehensive taxonomy to classify and characterize the state-of-the-art research in Android security research is contributed, resulting in the most comprehensive and elaborate investigation of the literature in this area of research.
Journal ArticleDOI
Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions
TL;DR: This paper provides a clear and comprehensive survey of the state-of-the-art work that detects malapps by characterizing behaviors of apps with various types of features, and highlights the issues of exploring effective features from apps, provide the taxonomy of these features and indicate the future directions.
Journal ArticleDOI
Machine learning-assisted signature and heuristic-based detection of malwares in Android devices
Zahoor-ur Rehman,Sidra Khan,Khan Muhammad,Jong Weon Lee,Zhihan Lv,Sung Wook Baik,Peer Azmat Shah,Khalid Mahmood Awan,Irfan Mehmood +8 more
TL;DR: An efficient hybrid framework is presented for detection of malware in Android Apps that considers both signature and heuristic-based analysis for Android Apps, and results show improved accuracy in malware detection.
Posted Content
Android Malware Detection using Deep Learning on API Method Sequences
TL;DR: MalDozer is proposed, an automatic Android malware detection and family attribution framework that relies on sequences classification using deep learning techniques that can serve as a ubiquitous malware detection system that is not only deployed on servers, but also on mobile and even IoT devices.
References
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Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
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Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Proceedings ArticleDOI
Crowdroid: behavior-based malware detection system for Android
TL;DR: The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware, showing the potential for avoiding the spreading of a detected malware to a larger community.
Proceedings ArticleDOI
An Android Application Sandbox system for suspicious software detection
TL;DR: An Android Application Sandbox (AASandbox) is proposed which is able to perform both static and dynamic analysis on Android programs to automatically detect suspicious applications and might be used to improve the efficiency of classical anti-virus applications available for the Android operating system.
Book ChapterDOI
PUMA: Permission Usage to Detect Malware in Android
Borja Sanz,Igor Santos,Carlos Laorden,Xabier Ugarte-Pedrero,Pablo García Bringas,Gonzalo Alvarez +5 more
TL;DR: PUMA, a new method for detecting malicious Android applications through machine-learning techniques by analysing the extracted permissions from the application itself, is presented.