Results 171 to 180 of about 4,196 (220)

Efficient feature ranked hybrid framework for android Iot malware detection. [PDF]

Sci Rep
Saeed NH, Hamza AA, Sobh MA, Bahaa-Eldin AM.   +3 more
europepmc   +1 more source

Improving malware detection performance using hybrid deep representation learning with heuristic search algorithms. [PDF]

Sci Rep
Anuradha A, Chouhan AS, Srinivas Rao S.
europepmc   +1 more source

DEFEAT: Android device behavior-based datasets for multi-stage APT. [PDF]

Data Brief
Jabar T, Al-Kadhimi AA, Singh MM.
europepmc   +1 more source

FL-MalDrift: a federated learning framework for malware detection under local concept drift. [PDF]

Sci Rep
Patel A, Tomar DS, Pateriya RK, Haripriya R.   +3 more
europepmc   +1 more source

Mobile malware detection method using improved GhostNetV2 with image enhancement technique. [PDF]

Sci Rep
Du Y, Gao C, Chen X, Cui M, Xu L, Ning A.   +5 more
europepmc   +1 more source

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Learning Android Malware

Proceedings of the 12th International Conference on Availability, Reliability and Security, 2017
The number of Android malware is increasing every day. Thus Android malware detection is nowadays a big challenge. One of the most tedious tasks in malware detection is the extraction of malicious behaviors. This task is usually done manually and requires a huge effort of engineering. To avoid this step, we propose in this paper to use machine learning
Khanh-Huu-The Dam, Tayssir Touili
openaire   +1 more source

Anatomizing Android Malwares

2019 26th Asia-Pacific Software Engineering Conference (APSEC), 2019
Android OS being the popular choice of majority users also faces the constant risk of breach of confidentiality, integrity and availability (CIA). Effective mitigation efforts needs to identified in order to protect and uphold the CIA triad model, within the android ecosystem.
Anand Tirkey, Ramesh Kumar Mohapatra, Lov Kumar   +2 more
openaire   +1 more source

Identifying Android malware instructions

2014 IEEE Latin-America Conference on Communications (LATINCOM), 2014
Android is a very attractive platform for malware developers because it is widely used. There is a need to understand how malware works and how it can exploit a system's security architecture. To do so, this work decompiles Android malware applications to study their source code and to look for patterns, regarding instructions, method calls, and ...
Laura Victoria Morales Medina, Sandra Julieta Rueda   +1 more
openaire   +1 more source

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 ...
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An Analysis of Android Malware Behavior

2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), 2018
Android is dominating the smartphone market with more users than any other mobile operating system. But with its growing popularity, interest from attackers has also increased, as the number of malicious applications keeps on rising. To know more about these applications, investigation of their behavior has become very important.
Fehmi Jaafar, Gagandeep Singh, Pavol Zavarsky   +2 more
openaire   +1 more source