Intelligent approach for the industrialization of deep learning solutions applied to fault detection
Early fault detection, both in equipment and the products in process, is of paramount
importance in industrial processes to ensure the quality of the final product, avoid abnormal …
importance in industrial processes to ensure the quality of the final product, avoid abnormal …
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 …
for cybercriminals to conduct potentially more destructive cyberattacks; as a result, 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] Enhancing IoT network security through deep learning-powered Intrusion Detection System
The rapid growth of the Internet of Things (IoT) has brought about a global concern for the
security of interconnected devices and networks. This necessitates the use of efficient …
security of interconnected devices and networks. This necessitates the use of efficient …
An efficient hybrid-dnn for ddos detection and classification in software-defined iiot networks
Software-defined networking (SDN)-based Industrial Internet of Things (IIoT) networks have
a centralized controller that is a single attractive target for unauthorized users to attack …
a centralized controller that is a single attractive target for unauthorized users to attack …
[HTML][HTML] A survey on neural networks for (cyber-) security and (cyber-) security of neural networks
M Pawlicki, R Kozik, M Choraś - Neurocomputing, 2022 - Elsevier
The goal of this systematic and broad survey is to present and discuss the main challenges
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
that are posed by the implementation of Artificial Intelligence and Machine Learning in the …
A novel optimization based deep learning with artificial intelligence approach to detect intrusion attack in network system
S Siva Shankar, BT Hung, P Chakrabarti… - Education and …, 2024 - Springer
Modern life is increasingly influenced by networks, making cybersecurity a crucial area of
study. However, due to their few resources and varied makeup, they are more vulnerable to …
study. However, due to their few resources and varied makeup, they are more vulnerable to …
Differential evolution-based convolutional neural networks: An automatic architecture design method for intrusion detection in industrial control systems
Industrial control systems (ICSs) are facing serious and evolving security threats because of
a variety of malicious attacks. Deep learning-based intrusion detection systems (IDSs) have …
a variety of malicious attacks. Deep learning-based intrusion detection systems (IDSs) have …
A LSTM-FCNN based multi-class intrusion detection using scalable framework
Abstract Machine learning methods are widely used to implement intrusion detection models
for detecting and classifying intrusions in a network or a system. However, many challenges …
for detecting and classifying intrusions in a network or a system. However, many challenges …
[HTML][HTML] A novel optimized probabilistic neural network approach for intrusion detection and categorization
Nowadays, the web provides all of the nation's daily necessities, and time spent online is
rising quickly. The Internet is being used more widely than ever. As a result, cyberattacks …
rising quickly. The Internet is being used more widely than ever. As a result, cyberattacks …