Machine learning approaches to IoT security: A systematic literature review
With the continuous expansion and evolution of IoT applications, attacks on those IoT
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …
applications continue to grow rapidly. In this systematic literature review (SLR) paper, our …
[HTML][HTML] Intrusion detection in internet of things systems: a review on design approaches leveraging multi-access edge computing, machine learning, and datasets
The explosive growth of the Internet of Things (IoT) applications has imposed a dramatic
increase of network data and placed a high computation complexity across various …
increase of network data and placed a high computation complexity across various …
Building an efficient intrusion detection system based on feature selection and ensemble classifier
Y Zhou, G Cheng, S Jiang, M Dai - Computer networks, 2020 - Elsevier
Intrusion detection system (IDS) is one of extensively used techniques in a network topology
to safeguard the integrity and availability of sensitive assets in the protected systems …
to safeguard the integrity and availability of sensitive assets in the protected systems …
Hybrid intrusion detection using mapreduce based black widow optimized convolutional long short-term memory neural networks
PR Kanna, P Santhi - Expert Systems with Applications, 2022 - Elsevier
The recent advancements in information and communication technologies have led to an
increasing number of online systems and services. These online systems can utilize …
increasing number of online systems and services. These online systems can utilize …
Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features
PR Kanna, P Santhi - Knowledge-Based Systems, 2021 - Elsevier
Intrusion detection systems (IDS) differentiate the malicious entries from the legitimate
entries in network traffic data and helps in securing the networks. Deep learning algorithms …
entries in network traffic data and helps in securing the networks. Deep learning algorithms …
Deep learning in IoT intrusion detection
Abstract The Internet of Things (IoT) is the new paradigm of our times, where smart devices
and sensors from across the globe are interconnected in a global grid, and distributed …
and sensors from across the globe are interconnected in a global grid, and distributed …
As-ids: Anomaly and signature based ids for the internet of things
Abstract The Internet of Things (IoT) is a massively extensive environment that can manage
many diverse applications. Security is critical due to potential malicious threats and the …
many diverse applications. Security is critical due to potential malicious threats and the …
A hybrid intrusion detection system based on scalable K-means+ random forest and deep learning
C Liu, Z Gu, J Wang - Ieee Access, 2021 - ieeexplore.ieee.org
Digital assets have come under various network security threats in the digital age. As a kind
of security equipment to protect digital assets, intrusion detection system (IDS) is less …
of security equipment to protect digital assets, intrusion detection system (IDS) is less …
[HTML][HTML] Attacks to automatous vehicles: A deep learning algorithm for cybersecurity
THH Aldhyani, H Alkahtani - Sensors, 2022 - mdpi.com
Rapid technological development has changed drastically the automotive industry. Network
communication has improved, helping the vehicles transition from completely machine-to …
communication has improved, helping the vehicles transition from completely machine-to …
A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions
H Ahmetoglu, R Das - Internet of Things, 2022 - Elsevier
Rapid developments in network technologies and the amount and scope of data transferred
on networks are increasing day by day. Depending on this situation, the density and …
on networks are increasing day by day. Depending on this situation, the density and …