Machine learning based solutions for security of Internet of Things (IoT): A survey

SM Tahsien, H Karimipour, P Spachos - Journal of Network and Computer …, 2020 - Elsevier
Over the last decade, IoT platforms have been developed into a global giant that grabs every
aspect of our daily lives by advancing human life with its unaccountable smart services …

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 …

Machine learning for 6G wireless networks: Carrying forward enhanced bandwidth, massive access, and ultrareliable/low-latency service

J Du, C Jiang, J Wang, Y Ren… - IEEE Vehicular …, 2020 - ieeexplore.ieee.org
To satisfy the expected plethora of demanding services, the future generation of wireless
networks (6G) has been mandated as a revolutionary paradigm to carry forward the …

Selection of effective machine learning algorithm and Bot-IoT attacks traffic identification for internet of things in smart city

M Shafiq, Z Tian, Y Sun, X Du, M Guizani - Future Generation Computer …, 2020 - Elsevier
Identifying cyber attacks traffic is very important for the Internet of things (IoT) security in
smart city. Recently, the research community in the field of IoT Security endeavor hard to …

IoT malicious traffic identification using wrapper-based feature selection mechanisms

M Shafiq, Z Tian, AK Bashir, X Du, M Guizani - Computers & Security, 2020 - Elsevier
Abstract Machine Learning (ML) plays very significant role in the Internet of Things (IoT)
cybersecurity for malicious and intrusion traffic identification. In other words, ML algorithms …

Deep reinforcement learning for user association and resource allocation in heterogeneous cellular networks

N Zhao, YC Liang, D Niyato, Y Pei… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Heterogeneous cellular networks can offload the mobile traffic and reduce the deployment
costs, which have been considered to be a promising technique in the next-generation …

IoT security techniques based on machine learning: How do IoT devices use AI to enhance security?

L Xiao, X Wan, X Lu, Y Zhang… - IEEE Signal Processing …, 2018 - ieeexplore.ieee.org
The Internet of things (IoT), which integrates a variety of devices into networks to provide
advanced and intelligent services, has to protect user privacy and address attacks such as …

Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems

S Shen, X Wu, P Sun, H Zhou, Z Wu, S Yu - Expert Systems with …, 2023 - Elsevier
Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends
requests to gather private data from an edge-computing-based IoT cloud storage system …

Learning-based computation offloading for IoT devices with energy harvesting

M Min, L Xiao, Y Chen, P Cheng, D Wu… - IEEE Transactions on …, 2019 - ieeexplore.ieee.org
Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy
harvesting (EH) to provide high-level experiences for computational intensive applications …

A deep recurrent neural network based approach for internet of things malware threat hunting

H HaddadPajouh, A Dehghantanha, R Khayami… - Future Generation …, 2018 - Elsevier
Abstract Internet of Things (IoT) devices are increasingly deployed in different industries and
for different purposes (eg sensing/collecting of environmental data in both civilian and …