[HTML][HTML] The rise of obfuscated Android malware and impacts on detection methods

WF Elsersy, A Feizollah, NB Anuar - PeerJ Computer Science, 2022 - peerj.com
The various application markets are facing an exponential growth of Android malware. Every
day, thousands of new Android malware applications emerge. Android malware hackers …

A comprehensive review on permissions-based Android malware detection

Y Sharma, A Arora - International Journal of Information Security, 2024 - Springer
The first Android-ready “G1” phone debuted in late October 2008. Since then, the growth of
Android malware has been explosive, analogous to the rise in the popularity of Android. The …

ICMFKC with optimize XGBoost classification for breast cancer image screening and detection

A Babu, SA Jerome - Multimedia Tools and Applications, 2024 - Springer
Nowadays most vicious disease is cancer, the cure of which must be the main argument
through scientific investigation. The prior detection of cancer could assist in curing the …

Hybrid Fuzzy Archimedes‐based Light GBM‐XGBoost model for distributed task scheduling in mobile edge computing

G Kumaresan, K Devi, S Shanthi… - Transactions on …, 2023 - Wiley Online Library
Mobile edge computing (MEC) mainly offers strong computing capabilities and functions to
finish the delay‐sensitive task in time with the help of 5G wireless networks. Task scheduling …

[HTML][HTML] Novel hybrid classifier based on fuzzy type-III decision maker and ensemble deep learning model and improved chaos game optimization

N Mehrabi Hashjin, MH Amiri, A Mohammadzadeh… - Cluster …, 2024 - Springer
This paper presents a unique hybrid classifier that combines deep neural networks with a
type-III fuzzy system for decision-making. The ensemble incorporates ResNet-18, Efficient …

[HTML][HTML] Fuzzy integral-based multi-classifiers ensemble for android malware classification

A Taha, O Barukab, S Malebary - Mathematics, 2021 - mdpi.com
One of the most commonly used operating systems for smartphones is Android. The open-
source nature of the Android operating system and the ability to include third-party Android …

Androidgyny: Reviewing clustering techniques for Android malware family classification

TSR Pimenta, F Ceschin, A Gregio - Digital Threats: Research and …, 2024 - dl.acm.org
Thousands of malicious applications (apps) are created daily, modified with the aid of
automation tools, and released on the World Wide Web. Several techniques have been …

Superpixel for seagrass mapping: a novel method using PlanetScope imagery and machine learning in Tauranga harbour, New Zealand

NT Ha, HQ Nguyen, TD Pham, CT Hoang… - Environmental Earth …, 2023 - Springer
Seagrass ecosystem provides valuable ecosystem services and is significant blue carbon
sink. This resource, however, has been degraded across the globe with a loss rate of 7 …

ModZoo: A Large-Scale Study of Modded Android Apps and their Markets

LA Saavedra, HS Dutta, AR Beresford… - arXiv preprint arXiv …, 2024 - arxiv.org
We present the results of the first large-scale study into Android markets that offer modified
or modded apps: apps whose features and functionality have been altered by a third-party …

[PDF][PDF] R-mfdroid: Android malware detection using ranked manifest file components

K Khariwal, R Gupta, J Singh, A Arora - International Journal of …, 2021 - academia.edu
With the increasing fame of Android OS over the past few years, the quantity of malware
assaults on Android has additionally expanded. In the year 2018, around 28 million …