Results 91 to 100 of about 20,046 (244)

Android Malware Detection by Correlated Real Permission Couples Using FP Growth Algorithm and Neural Networks [PDF]

, 2023
Abhinandan Banik, Jyoti Prakash Singh
openalex   +1 more source

CLASSIFYING ANDROID MALWARE CATEGORIES BASED ON DYNAMIC FEATURES: AN INTEGRATION OF FEATURE REDUCTION AND SELECTION TECHNIQUES

Mağallaẗ Al-kūfaẗ Al-handasiyyaẗ
Android malware has grown steadily into a major internet threat. Despite efforts to identify and categorize malware in seemingly safe Android apps, addressing this issue is still lacking.
abdullah alsraratee, Ahmed Al-Azawei
doaj   +1 more source

WinDroid: A Novel Framework for Windows and Android Malware Family Classification Using Hierarchical Ensemble Support Vector Machines With Multiview Handcrafted and Deep Learning Features

IET Information Security, Volume 2025, Issue 1, 2025.
The rapid growth and diversification of malware variants, driven by advanced code obfuscation, evasion, and antianalysis techniques, present a significant threat to cybersecurity. The inadequacy of traditional methods in accurately classifying these evolving threats highlights the need for effective and robust malware classification techniques.
K. Sundara Krishnan, S. Syed Suhaila, Hyun-A Park   +2 more
wiley   +1 more source

Why an Android App is Classified as Malware? Towards Malware Classification Interpretation [PDF]

, 2020
Bozhi Wu, Sen Chen, Cuiyun Gao, Lingling Fan, Yang Liu, Weiping Wen, Michael R. Lyu   +6 more
openalex   +1 more source

HTTP behavior characteristics generation and extraction approach for Android malware

Dianxin kexue, 2016
Growing of Android malware，not only seriously endangered the security of the Android market，but also brings challenges for detection.A generation and extraction approach of automatic Android malware behavioral signatures was proposed based on HTTP ...
Yaling LUO, Wenwei LI, Xin SU
doaj   +2 more sources

A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis [PDF]

, 2019
Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner.
Almeida, Mario, Blackburn, Jeremy, Cristofaro, Emiliano De, Mariconti, Enrico, Onwuzurike, Lucky, Stringhini, Gianluca   +5 more
core  

Malware Analysis on Android

, 2021
In the XXI century, the world has witnessed the creation, development and proliferation of mobile devices until the massive usage apparent nowadays. The portability, instantaneity and ease of use that these devices offer has encouraged the great majority of the population to have one of them at arm’s length.
Puente Arribas, Daniel, Daguerre Garrido, José Ignacio, Costales de Ledesma, Ramón   +2 more
openaire   +1 more source

MMF: A Lightweight Approach of Multimodel Fusion for Malware Detection

IET Software, Volume 2025, Issue 1, 2025.
Nowadays, the Android system is widely used in mobile devices. The existence of malware in the Android system has posed serious security risks. Therefore, detecting malware has become a main research focus for Android devices. The existing malware detection methods include those based on static analysis, dynamic analysis, and hybrid analysis.
Bo Yang, Mengbo Li, Li Li, Huai Liu, Nadeem Sarwar   +4 more
wiley   +1 more source

Explaining the Use of Cryptographic API in Android Malware [PDF]

, 2023
Adam Janovsky, Davide Maiorca, Dominik Macko, Vashek Matyáš, Giorgio Giacinto   +4 more
openalex   +1 more source

KTSDroid: A Framework for Android Malware Categorization Using the Kernel Task Structure [PDF]

, 2023
Saneeha Khalid, Khalid Muhammad Imran, Faisal Bashir Hussain   +2 more
openalex   +1 more source