Results 111 to 120 of about 20,046 (244)

MalQwen: Fine Tuned LLM for Static Android Malware Analysis Report

IEEE Access
The Android operating system continues to face escalating security challenges, primarily due to its open-source nature and the rapid proliferation of applications from untrusted sources.
Tegar Ganang Satrio Priambodo, Angela Oryza Prabowo, Annisa Dwi Puspitarini, Raihan Adam Handoyo Winarso, Nur Aisyah, Mohammad Yoga Pratama, Diana Purwitasari, Baskoro Adi Pratomo   +7 more
doaj   +1 more source

Explainable Machine Learning for Malware Detection on Android Applications

Information
The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device.
Catarina Palma, Artur Ferreira, Mário Figueiredo   +2 more
doaj   +1 more source

Obfuscated Malware Detection and Classification in Network Traffic Leveraging Hybrid Large Language Models and Synthetic Data

Sensors
Android malware detection remains a critical issue for mobile security. Cybercriminals target Android since it is the most popular smartphone operating system (OS). Malware detection, analysis, and classification have become diverse research areas.
Mehwish Naseer, Farhan Ullah, Samia Ijaz, Hamad Naeem, Amjad Alsirhani, Ghadah Naif Alwakid, Abdullah Alomari   +6 more
doaj   +1 more source

Android malware detection with MH-100K: An innovative dataset for advanced research. [PDF]

Data Brief, 2023
Bragança H, Rocha V, Barcellos L, Souto E, Kreutz D, Feitosa E.   +5 more
europepmc   +1 more source

Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm. [PDF]

Sensors (Basel), 2023
Babbar H, Rani S, Sah DK, AlQahtani SA, Kashif Bashir A.   +4 more
europepmc   +1 more source

Android Dynamic Malware Analysis

International Journal for Research in Applied Science and Engineering Technology
In today’s mobile computing landscape, Android-based systems are highly prevalent and frequently targeted by malicious applications that exhibit anomalous behavior. Detecting such anomalies in real time is critical for ensuring system stability, user data privacy, and overall device security. This review paper explores the implementation and evaluation
openaire   +1 more source

AndroDFA: Android Malware Classification Based on Resource Consumption [PDF]

, 2020
Luca Massarelli, Leonardo Aniello, Claudio Ciccotelli, Leonardo Querzoni, Daniele Ucci, Roberto Baldoni   +5 more
openalex   +1 more source

Comprehensive Analysis of Android Malware detection through Semi-supervised Autoencoder models [PDF]

, 2023
Neda Firoz, Abdullah Bin Firoz, Mohammad Sadman Tahsin   +2 more
openalex   +1 more source

FedDroidMeter: A Privacy Risk Evaluator for FL-Based Android Malware Classification Systems. [PDF]

Entropy (Basel), 2023
Jiang C, Xia C, Liu Z, Wang T.
europepmc   +1 more source

Android Malware Detection Using Autoencoder

, 2019
9 Pages, 4 Figures, 3 ...
Naway, Abdelmonim, Li, Yuancheng
openaire   +2 more sources