Results 101 to 110 of about 2,901,102 (167)
A pragmatic android malware detection procedure
Computers & Security, 2017 Abstract The academic security research community has studied the Android malware detection problem extensively. Machine learning methods proposed in previous work typically achieve high reported detection performance on fixed datasets. Some of them also report reasonably fast prediction times.
Palumbo, Paolo +5 more
openaire +1 more source
An Improved Malicious Application Detection in Social Networks (MADSN)
مجلة جامعة الزيتونة, 2021 Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absence of third-party market regulation has prompted research institutions to propose different malware ...
Nagmden Nasser, Adel Abosdel
doaj
Boutique Malware – Custom made for e-business [PDF]
, 2010 Malware are typically known through extensive publicity in the media when incidents such as infection by Conficker on the computers around the globe. Such Malware infects all who are vulnerable to its bite.
Fung, C.C., Pan, J.Y.
core
Android Malware Detection Using Backpropagation Neural Network [PDF]
, 2016 The rapid growing adoption of android operating system around the world affects the growth of malware that attacks this platform. One possible solution to overcome the threat of malware is building a comprehensive system to detect existing malware.
Herman Tolle +5 more
core +1 more source
Egyptian Informatics JournalThe growing complexity of cyber threats has shifted the focus from merely identifying threats to detecting their origins, resulting in stronger defenses against malware.
Collins Chimeleze +3 more
doaj +1 more source
IEEE Access, 2019 Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware.
Zhuo Ma +4 more
doaj +1 more source
Android malware detection with unbiased confidence guarantees
Neurocomputing, 2018 The impressive growth of smartphone devices in combination with the rising ubiquity of using mobile platforms for sensitive applications such as Internet banking, have triggered a rapid increase in mobile malware. In recent literature, many studies examine Machine Learning techniques, as the most promising approach for mobile malware detection, without
Harris Papadopoulos +3 more
openaire +4 more sources
AAGAN: Android Malware Generation System Based on Generative Adversarial Network
Vietnam Journal of Computer ScienceWith the rapid evolution of mobile malware, especially Android malware, machine learning (ML)-based Android malware detection systems have drawn massive attention. Although ML algorithms have recently led to many vital breakthroughs in malware detection,
Doan Minh Trung +4 more
doaj +1 more source
Android Malware Detection Based on Factorization Machine
IEEE Access, 2019 As the popularity of Android smart phones has increased in recent years, so too has the number of malicious applications. Due to the potential for data theft mobile phone users face, the detection of malware on Android devices has become an increasingly important issue in cyber security.
Chenglin Li +5 more
openaire +5 more sources
Exploring new methods for detecting Android malware
تحلیل مدارها، داده ها و سامانه هاAndroid malware detection has consistently faced numerous challenges due to the continuous evolution of hacking and penetration techniques targeting the Android operating system. Consequently, many classic and legacy methods for detecting Android malware
SeyedAlireza Khayami +1 more
doaj +1 more source