Results 161 to 170 of about 16,685 (199)

Classifying Android Malware through Subgraph Mining

2014
Current smartphones are based upon the concept of apps, which are lightweight applications that are distributed through on-line marketplaces, such as Google Play (for Android devices). Unfortunately, this market-centric model is affected by several major security and trust issues, due to the fact that anyone can easily create, and deploy through the ...
Fabio Martinelli, Andrea Saracino, Daniele Sgandurra   +2 more
openaire   +3 more sources

Fingerprinting Android Malware Packages

2021
A fuzzy (hashing) or approximate fingerprint of binary software is a digest that captures its static content, in similar manner to cryptographic hashing fingerprints such as MD5 and SHA1. Still, the fuzzy fingerprint change is virtually linear to the change in the binary content. In other words, smaller changes in the static content of the malware will
ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb   +3 more
openaire   +1 more source

Android malware classification method

Proceedings of the 2013 Research in Adaptive and Convergent Systems, 2013
The number of Android malware is increasing with the growth of Android, so there needs to have a method to classify malware families. There are many classification methods proposed so far, but most of them are based on permission information such as the number of requested permissions and critical permissions.
Byeongho Kang, BooJoong Kang, Jungtae Kim, Eul Gyu Im   +3 more
openaire   +1 more source

Fingerprinting Android malware families

Frontiers of Computer Science, 2018
The domination of the Android operating system in the market share of smart terminals has engendered increasing threats of malicious applications (apps). Research on Android malware detection has received considerable attention in academia and the industry.
Nannan Xie, Xing Wang, Wei Wang, Jiqiang Liu   +3 more
openaire   +1 more source

Composition-Malware: Building Android Malware at Run Time

2015 10th International Conference on Availability, Reliability and Security, 2015
We present a novel model of malware for Android, named composition-malware, which consists of composing fragments of code hosted on different and scattered locations at run time. An key feature of the model is that the malicious behavior could dynamically change and the payload could be activated under logic or temporal conditions.
Gerardo Canfora, Francesco Mercaldo, Giovanni Moriano, Visaggio C   +3 more
openaire   +2 more sources

Android malware and mitigations

Network Security, 2012
Just as the ubiquitous nature of Windows made it an enticing target for malware writers and cyber-criminals, so it is with Android. The maliciously inclined have not been slow to exploit the popularity of the platform. Steve Mansfield-Devine examines the nature of the malware problem and how Google's open approach to distribution makes implementing ...
openaire   +1 more source

Android Malware Detection

2023 World Conference on Communication & Computing (WCONF), 2023
N Gagan, S Sai Kumar, M Keerthana, Samarth S Vaidya, Vanshika Rastogi   +4 more
openaire   +1 more source

Identifying Android Malware Families Using Android-Oriented Metrics

2019 IEEE International Conference on Big Data (Big Data), 2019
Android malware (malicious apps) families share common attributes and behavior through sharing core malicious code. However, as the number of new malware increases, the task of identifying the correct family becomes more challenging. Two prominent approaches tackle this problem, either using dynamic analysis that captures the runtime behavior of the ...
William Blanc, Lina G. Hashem, Karim O. Elish, M. J. Hussain Almohri   +3 more
openaire   +1 more source

CNN-Based Android Malware Detection

2017 International Conference on Software Security and Assurance (ICSSA), 2017
The growth in mobile devices has exponentially increased, making information easy to access but at the same time vulnerable. Malicious applications can gain access to sensitive and critical user information by exploiting unsolicited permission controls.
Meenu Ganesh, Priyanka Pednekar, Pooja Prabhuswamy, Divyashri Sreedharan Nair, Younghee Park, Hyeran Jeon   +5 more
openaire   +1 more source

Android malware analysis and conceptual malware mitigation approaches

2016 International Conference on Information and Communication Technology Convergence (ICTC), 2016
Mobile devices have enjoyed unprecedented growth in the last decade. As devices become more ubiquitous and users place more sensitive data on their devices, the amount of mobile malware in the wild has grown. In order to protect mobile device users and their data, mobile security solutions have been brought to market for consumer and enterprise users ...
Tae Oh, Hwa Shin Moon, Young Ho Kim, Jeong Neyo Kim, Bill Stackpole   +4 more
openaire   +1 more source