A comprehensive review on malware detection approaches

ÖA Aslan, R Samet - IEEE access, 2020 - ieeexplore.ieee.org
According to the recent studies, malicious software (malware) is increasing at an alarming
rate, and some malware can hide in the system by using different obfuscation techniques. In …

A review of android malware detection approaches based on machine learning

K Liu, S Xu, G Xu, M Zhang, D Sun, H Liu - IEEE access, 2020 - ieeexplore.ieee.org
Android applications are developing rapidly across the mobile ecosystem, but Android
malware is also emerging in an endless stream. Many researchers have studied the …

A long short‐term memory‐based model for greenhouse climate prediction

Y Liu, D Li, S Wan, F Wang, W Dou, X Xu… - … Journal of Intelligent …, 2022 - Wiley Online Library
Greenhouses can grow many off‐season vegetables and fruits, which improves people's
quality of life. Greenhouses can also help crops resist natural disasters and ensure the …

An overview of Internet of Things (IoT): Architectural aspects, challenges, and protocols

BB Gupta, M Quamara - Concurrency and Computation …, 2020 - Wiley Online Library
Understanding of any computing environment requires familiarity with its underlying
technologies. Internet of Things (IoT), being a new era of computing in the digital world, aims …

BSeIn: A blockchain-based secure mutual authentication with fine-grained access control system for industry 4.0

C Lin, D He, X Huang, KKR Choo… - Journal of network and …, 2018 - Elsevier
To be prepared for the 'Industry 4.0'-era, we propose a hierarchical framework comprising
four tangible layers, which is designed to vertically integrate inter-organizational value …

FED-IIoT: A robust federated malware detection architecture in industrial IoT

R Taheri, M Shojafar, M Alazab… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
The sheer volume of industrial Internet of Things (IIoT) malware is one of the most serious
security threats in today's interconnected world, with new types of advanced persistent …

GDroid: Android malware detection and classification with graph convolutional network

H Gao, S Cheng, W Zhang - Computers & Security, 2021 - Elsevier
The dramatic increase in the number of malware poses a serious challenge to the Android
platform and makes it difficult for malware analysis. In this paper, we propose a novel …

When machine learning meets privacy in 6G: A survey

Y Sun, J Liu, J Wang, Y Cao… - … Surveys & Tutorials, 2020 - ieeexplore.ieee.org
The rapid-developing Artificial Intelligence (AI) technology, fast-growing network traffic, and
emerging intelligent applications (eg, autonomous driving, virtual reality, etc.) urgently …

Intelligent mobile malware detection using permission requests and API calls

M Alazab, M Alazab, A Shalaginov, A Mesleh… - Future Generation …, 2020 - Elsevier
Malware is a serious threat that has been used to target mobile devices since its inception.
Two types of mobile malware attacks are standalone: fraudulent mobile apps and injected …

Data mining and machine learning methods for sustainable smart cities traffic classification: A survey

M Shafiq, Z Tian, AK Bashir, A Jolfaei, X Yu - Sustainable Cities and …, 2020 - Elsevier
This survey paper describes the significant literature survey of Sustainable Smart Cities
(SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection …