Detection of android malware based on deep forest and feature enhancement

X Zhang, J Wang, J Xu, C Gu - IEEE Access, 2023 - ieeexplore.ieee.org
Detecting Android malware in its spread or download stage is a challenging work, which can
realize early detection of malware before it reaches user side. In this paper, we propose a …

A comparative study on detection of malware and benign on the internet using machine learning classifiers

J Pavithra, S Selvakumara Samy - Mathematical Problems in …, 2022 - Wiley Online Library
The exponential growth in network usage has opened the way for people who use the
Internet to be exploited. A phishing attack is the most effective way to obtain sensitive …

Machine learning based malware detection in cloud environment using clustering approach

R Kumar, K Sethi, N Prajapati… - 2020 11th …, 2020 - ieeexplore.ieee.org
Enforcing security and resilience in a cloud platform is an essential but challenging problem
due to the presence of a large number of heterogeneous applications running on shared …

Machine learning classification algorithms for adware in android devices: a comparative evaluation and analysis

JY Ndagi, JK Alhassan - 2019 15th International Conference on …, 2019 - ieeexplore.ieee.org
Exponential growth experienced in Internet usage has paved the way to exploit users of the
Internet, a phishing attack is one of the means that can be used to obtained victim …

[PDF][PDF] Intersection Features for Android Botnet Classification

NS Ismail, R Yusof, H Saad, MF Abdollah - academia.edu
The evolution of the Internet of things (IoT) has made a significant impact and availed
opportunities for mobile device usage on human life. Many of IoT devices will be supposedly …

Système de détection de malwares basé sur l'apprentissage profond pour Android

RC Maini - 2020 - dspace.univ-tebessa.dz
Le système d'exploitation Android est devenu le système d'exploitation mobile le plus utilisé,
cette popularité croissante a attiré l'attention du développeur des malwares, ce qui leur …