Toward software-defined networking-based IoT frameworks: A systematic literature review, taxonomy, open challenges and prospects

S Siddiqui, S Hameed, SA Shah, I Ahmad… - IEEE …, 2022 - ieeexplore.ieee.org
Internet of Things (IoT) is characterized as one of the leading actors for the next evolutionary
stage in the computing world. IoT-based applications have already produced a plethora of …

A systematic review on Deep Learning approaches for IoT security

L Aversano, ML Bernardi, M Cimitile, R Pecori - Computer Science Review, 2021 - Elsevier
The constant spread of smart devices in many aspects of our daily life goes hand in hand
with the ever-increasing demand for appropriate mechanisms to ensure they are resistant …

Design and development of a deep learning-based model for anomaly detection in IoT networks

I Ullah, QH Mahmoud - IEEE Access, 2021 - ieeexplore.ieee.org
The growing development of IoT (Internet of Things) devices creates a large attack surface
for cybercriminals to conduct potentially more destructive cyberattacks; as a result, the …

[HTML][HTML] Towards a machine learning-based framework for DDOS attack detection in software-defined IoT (SD-IoT) networks

J Bhayo, SA Shah, S Hameed, A Ahmed, J Nasir… - … Applications of Artificial …, 2023 - Elsevier
Abstract The Internet of Things (IoT) is a complex and diverse network consisting of resource-
constrained sensors/devices/things that are vulnerable to various security threats …

Machine learning enabled techniques for protecting wireless sensor networks by estimating attack prevalence and device deployment strategy for 5G networks

P Kumar, A Baliyan, KR Prasad… - Wireless …, 2022 - Wiley Online Library
A number of disadvantages of traditional networks may be attributed to the close relationship
that exists between the control plane and the data plane inside proprietary hardware …

Detection of DDoS attacks with feed forward based deep neural network model

AE Cil, K Yildiz, A Buldu - Expert Systems with Applications, 2021 - Elsevier
As a result of the increase in the services provided over the internet, it is seen that the
network infrastructure is more exposed to cyber attacks. The most widely used of these …

A novel two-stage deep learning model for network intrusion detection: LSTM-AE

V Hnamte, H Nhung-Nguyen, J Hussain… - Ieee …, 2023 - ieeexplore.ieee.org
Machine learning and deep learning techniques are widely used to evaluate intrusion
detection systems (IDS) capable of rapidly and automatically recognizing and classifying …

The DDoS attacks detection through machine learning and statistical methods in SDN

A Banitalebi Dehkordi, MR Soltanaghaei… - The Journal of …, 2021 - Springer
The distributed denial-of-service (DDoS) attack is a security challenge for the software-
defined network (SDN). The different limitations of the existing DDoS detection methods …

DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)

S Ur Rehman, M Khaliq, SI Imtiaz, A Rasool… - Future Generation …, 2021 - Elsevier
Abstract Distributed Denial of Service (DDoS) attacks can put the communication networks
in instability by throwing malicious traffic and requests in bulk over the network. Computer …

Realizing an efficient IoMT-assisted patient diet recommendation system through machine learning model

C Iwendi, S Khan, JH Anajemba, AK Bashir… - IEEE access, 2020 - ieeexplore.ieee.org
Recent studies have shown that robust diets recommended to patients by Dietician or an
Artificial Intelligent automated medical diet based cloud system can increase longevity …