Results 81 to 90 of about 1,195 (175)

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

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

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

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

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

A Modified ResNeXt for Android Malware Identification and Classification. [PDF]

Comput Intell Neurosci, 2022
Albahar MA, ElSayed MS, Jurcut A.
europepmc   +1 more source

Automated Android Malware Detection Using User Feedback. [PDF]

Sensors (Basel), 2022
Duque J, Mendes G, Nunes L, de Almeida A, Serrão C.   +4 more
europepmc   +1 more source