Results 91 to 100 of about 16,685 (199)

Feature Graph Construction With Static Features for Malware Detection

IET Information Security, Volume 2025, Issue 1, 2025.
Malware can greatly compromise the integrity and trustworthiness of information and is in a constant state of evolution. Existing feature fusion‐based detection methods generally overlook the correlation between features. And mere concatenation of features will reduce the model’s characterization ability, lead to low detection accuracy. Moreover, these
Binghui Zou, Chunjie Cao, Longjuan Wang, Yinan Cheng, Chenxi Dang, Ying Liu, Jingzhang Sun, Stelvio Cimato   +7 more
wiley   +1 more source

AMALGAN: Image‐Based Android Malware Classification Using Generative Adversarial Network

The Journal of Engineering
The Android malware detection process requires analysing numerous files to ensure system security. Malware can also be embedded in media files and images.
Zahid Hussain Qaisar, Muhammad Faheem, Muhammad Waqar Ashraf   +2 more
doaj   +1 more source

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

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

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

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

Not so Crisp, Malware! Fuzzy Classification of Android Malware Classes

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2018
Mobile devices have been spreading at great rate in recent years. Not only smartphone, but also tablets and IoT devices, are gaining an increasingly place in our everyday lives. This is the reason why attackers are developing more and more aggressive techniques with the aim to exfiltrate our sensitive and private information.
Mercaldo F., Saracino A.
openaire   +4 more sources

An Effective Temporal Convolutional Networks-Based Method for Detecting Android Malware Using Dynamic Extracted Features

IEEE Access
With an increase in the number and complexity of malware, traditional malware detection methods such as heuristic-based and signature-based ones have become less adequate, leaving user applications vulnerable.
Abdurraheem Joomye, Mee Hong Ling, Muhammed Basheer Jasser, Athirah Mohd Ramly, Kok-Lim Alvin Yau   +4 more
doaj   +1 more source

A3CM: Automatic Capability Annotation for Android Malware

IEEE Access, 2019
Android malware poses serious security and privacy threats to the mobile users. Traditional malware detection and family classification technologies are becoming less effective due to the rapid evolution of the malware landscape, with the emerging of so ...
Junyang Qiu, Jun Zhang, Wei Luo, Lei Pan, Surya Nepal, Yu Wang, Yang Xiang   +6 more
doaj   +1 more source

MADRAS-NET: A deep learning approach for detecting and classifying android malware using Linknet

Measurement: Sensors
Malware is an intentionally created malicious software that still poses a serious threat in cyberspace. Android malware has become one of the most significant online threats in recent years due to its increase in prevalence. Even though a lot of work has
Yi Wang, Shanshan Jia
doaj   +1 more source