Network Intrusion Detection: An IoT and Non IoT-Related Survey
The proliferation of the Internet of Things (IoT) is occurring swiftly and is all-encompassing.
The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent …
The cyber attack on Dyn in 2016 brought to light the notable susceptibilities of intelligent …
Data Communication Challenges of Connected and Automated Vehicles in Rural Areas
M Tavasoli, A Sarrafzadeh, M Khaleghi… - IEEE …, 2025 - ieeexplore.ieee.org
The integration of connected and automated vehicles (CAVs) into rural areas offers a
promising solution to the unique challenges posed by limited infrastructure. This paper …
promising solution to the unique challenges posed by limited infrastructure. This paper …
Advanced hybrid techniques for cyberattack detection and defense in IoT networks
ABSTRACT The Internet of Things (IoT) represents a vast network of devices connected to
the Internet, making it easier for users to connect to modern technology. However, the …
the Internet, making it easier for users to connect to modern technology. However, the …
Deep-Learning-Based Approach for IoT Attack and Malware Detection.
B Taşcı - Applied Sciences (2076-3417), 2024 - search.ebscohost.com
Abstract The Internet of Things (IoT), introduced by Kevin Ashton in the late 1990s, has
transformed technology usage globally, enhancing efficiency and convenience but also …
transformed technology usage globally, enhancing efficiency and convenience but also …
A critical assessment of interpretable and explainable machine learning for intrusion detection
There has been a large number of studies in interpretable and explainable ML for
cybersecurity, in particular, for intrusion detection. Many of these studies have significant …
cybersecurity, in particular, for intrusion detection. Many of these studies have significant …
Cyber attack detection in IOT-WSN devices with threat intelligence using hidden and connected layer based architectures
In this paper, cyber-attacks in IOT-WSN are detected through proposed optimized-Neural
Network algorithms such as (i) Equilibrium Optimizer Neural Network (EO-NN),(ii) Particle …
Network algorithms such as (i) Equilibrium Optimizer Neural Network (EO-NN),(ii) Particle …
GenVRAM: Dataset Generator for Vehicle to Roadside Attacks and Misbehavior
D Ramsamooj, P Sharma, H Liu - IEEE Access, 2024 - ieeexplore.ieee.org
The surge in the number of driverless cars highlights the necessity for enhanced
transportation safety and efficiency. Achieving fully autonomous driving depends on the …
transportation safety and efficiency. Achieving fully autonomous driving depends on the …
Adversarial robustness of deep reinforcement learning-based intrusion detection
Abstract Machine learning techniques, including Deep Reinforcement Learning (DRL),
enhance intrusion detection systems by adapting to new threats. However, DRL's reliance …
enhance intrusion detection systems by adapting to new threats. However, DRL's reliance …
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks
Network intrusion detection systems face significant challenges in identifying emerging
attack patterns, especially when limited data samples are available. To address this, we …
attack patterns, especially when limited data samples are available. To address this, we …
Random Under Sampling for Performance Improvement in Attack Detection on Internet of Vehicles Using Machine Learning
MAN Anargya, W Ghozi… - Jurnal Informatika …, 2025 - ejournal.poltekharber.ac.id
Abstract The Internet of Vehicles (IoV) technology is one of the advancements derived from
the Internet of Things (IoT) in the transportation sector, benefiting its users. However, the …
the Internet of Things (IoT) in the transportation sector, benefiting its users. However, the …