Results 1 to 10 of about 551 (95)

Automated Android Malware Detection Using User Feedback. [PDF]

Sensors (Basel), 2022
The widespread usage of mobile devices and their seamless adaptation to each user’s needs through useful applications (apps) makes them a prime target for malware developers. Malware is software built to harm the user, e.g., to access sensitive user data, such as banking details, or to hold data hostage and block user access. These apps are distributed
Duque J, Mendes G, Nunes L, de Almeida A, Serrão C.   +4 more
europepmc   +5 more sources

Z2F: Heterogeneous graph-based Android malware detection. [PDF]

PLoS One
Android malware is becoming more common, and its invasion of smart devices has brought immeasurable losses to people’s lives. Most existing Android malware detection methods extract Android features from the original application files without considering the high-order hidden information behind them, but these hidden information can reflect malicious ...
Ma Z, Luktarhan N.
europepmc   +4 more sources

Trends in Android Malware Detection [PDF]

Journal of Digital Forensics, Security and Law, 2013
This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and
Kaveh Shaerpour, Ali Dehghantanha, Ramlan Mahmod   +2 more
openaire   +4 more sources

Deep Android Malware Detection [PDF]

Proceedings of the Seventh ACM on Conference on Data and Application Security and Privacy, 2017
In this paper, we propose a novel android malware detection system that uses a deep convolutional neural network (CNN). Malware classification is performed based on static analysis of the raw opcode sequence from a disassembled program. Features indicative of malware are automatically learned by the network from the raw opcode sequence thus removing ...
McLaughlin, Niall; id_orcid 0000-0002-0917-9145, Martinez del Rincon, Jesus; id_orcid 0000-0002-9574-4138, Kang, BooJoong, Yerima, Suleiman, Miller, Paul, Sezer, Sakir, Safaeisemnani, Yeganeh, Trickel, Erik, Zhao, Ziming, Doupé, Adam, Joon Ahn, Gail   +10 more
openaire   +4 more sources

Explaining Black-box Android Malware Detection [PDF]

2018 26th European Signal Processing Conference (EUSIPCO), 2018
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on features statically extracted from the application, such as system calls and permissions.
Marco Melis, Davide Maiorca, Battista Biggio, Giorgio Giacinto, Fabio Roli   +4 more
openaire   +4 more sources

Android Malware Detection Using Deep Learning

, 2022
This chapter investigates the potential of deep learning architectures for Android malware detection, specifically convolutional neural networks (CNNs) using natural language processing (NLP) concepts. The proposed solution is based on static analysis of raw opcode sequences from disassembled programs and other complementary features such as API calls ...
Millar, Stuart, McLaughlin, Niall, Martinez-del-Rincon, Jesus, Miller, Paul   +3 more
openaire   +3 more sources

Android malware detection: a survey [PDF]

SCIENTIA SINICA Informationis, 2020
Android has become the most popular mobile operating system in the past ten years due to its three main advantages, namely, the openness of source code, richness of hardware selection, and millions of applications (apps). It is of no surprise that Android has become the major target of malware.
Le YU, Xiaohong GUAN, Ming FAN, Xiapu LUO, Ting LIU, Jun LIU   +5 more
openaire   +1 more source

Android malware detection based on sensitive patterns

Telecommunication Systems, 2022
Abstract In recent years, the rapid increase in the number and type of Android malware has brought great challenges and pressure to malware detection systems. As a widely used method in android malware detection, static detecting has been a hot topic in academia and industry.
Kang Liu, Guanghui Zhang, Xue Chen, Qing Liu, Linyu Peng, Yurui Liu   +5 more
openaire   +1 more source

Smart malware detection on Android [PDF]

Security and Communication Networks, 2015
AbstractNowadays, because of its increased popularity, Android is target to a growing number of attacks and malicious applications, with the purpose of stealing private information and consuming credit by subscribing to premium services. Most of the current commercial antivirus solutions use static signatures for malware detection, which may fail to ...
Laura Gheorghe, Bogdan Marin, Gary Gibson, Lucian Mogosanu, Razvan Deaconescu, Valentin‐Gabriel Voiculescu, Mihai Carabas   +6 more
openaire   +1 more source

Android Fragmentation in Malware Detection

Computers & Security, 2019
Abstract Differences between Android versions affect not only application developers but also make the task of securing Android harder, as it is not easy to keep track of updates. In this paper, we first systematically analyze the Android framework, which includes APIs and enforced manifest permissions to realize the inconsistency currently exists in
Long Nguyen-Vu, Jinung Ahn, Souhwan Jung
openaire   +1 more source