An experimental analysis of attack classification using machine learning in IoT networks
In recent years, there has been a massive increase in the amount of Internet of Things (IoT)
devices as well as the data generated by such devices. The participating devices in IoT …
devices as well as the data generated by such devices. The participating devices in IoT …
A novel and secure attacks detection framework for smart cities industrial internet of things
New trend of smart cities has changed the life with more equipped and integrated systems.
Various new technologies have adopted for sustainable and improved smart cities …
Various new technologies have adopted for sustainable and improved smart cities …
A deep learning methodology for predicting cybersecurity attacks on the internet of things
OA Alkhudaydi, M Krichen, AD Alghamdi - Information, 2023 - mdpi.com
With the increasing severity and frequency of cyberattacks, the rapid expansion of smart
objects intensifies cybersecurity threats. The vast communication traffic data between …
objects intensifies cybersecurity threats. The vast communication traffic data between …
Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications
Abstract Intrusion Detection Systems (IDS) based on deep learning models can identify and
mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models …
mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models …
ML-based IDPS enhancement with complementary features for home IoT networks
The Internet of Things (IoT) networks are obstructed by security vulnerabilities that hackers
can leverage to operate intrusions in many environments, such as smart homes, smart …
can leverage to operate intrusions in many environments, such as smart homes, smart …
A hybrid machine learning model for detecting cybersecurity threats in IoT applications
The introduction of the Internet of Things has led to the connectivity of millions of devices
with less human interaction. This demand in connectivity has resulted in a surge in network …
with less human interaction. This demand in connectivity has resulted in a surge in network …
Cyber-attacks detection in industrial systems using artificial intelligence-driven methods
Modern industrial systems and critical infrastructures are constantly exposed to malicious
cyber-attacks that are challenging and difficult to identify. Cyber-attacks can cause severe …
cyber-attacks that are challenging and difficult to identify. Cyber-attacks can cause severe …
Distributed deep neural-network-based middleware for cyber-attacks detection in smart IoT ecosystem: A novel framework and performance evaluation approach
Cyberattacks always remain the major threats and challenging issues in the modern digital
world. With the increase in the number of internet of things (IoT) devices, security challenges …
world. With the increase in the number of internet of things (IoT) devices, security challenges …
Intrusion Detection System to Advance Internet of Things Infrastructure‐Based Deep Learning Algorithms
H Alkahtani, THH Aldhyani - Complexity, 2021 - Wiley Online Library
Smart grids, advanced information technology, have become the favored intrusion targets
due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid …
due to the Internet of Things (IoT) using sensor devices to collect data from a smart grid …
A taxonomy of machine-learning-based intrusion detection systems for the internet of things: A survey
A Jamalipour, S Murali - IEEE Internet of Things Journal, 2021 - ieeexplore.ieee.org
The Internet of Things (IoT) is an emerging technology that has earned a lot of research
attention and technical revolution in recent years. Significantly, IoT connects and integrates …
attention and technical revolution in recent years. Significantly, IoT connects and integrates …