Machine learning and artificial neural network accelerated computational discoveries in materials science
Artificial intelligence (AI) has been referred to as the “fourth paradigm of science,” and as
part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically …
part of a coherent toolbox of data‐driven approaches, machine learning (ML) dramatically …
IoT security with Deep Learning-based Intrusion Detection Systems: A systematic literature review
In the recent years, Internet of things (IoT) is rising increasingly to become a big research
topic due to the billions of devices dispatched around the world. These devices are …
topic due to the billions of devices dispatched around the world. These devices are …
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 …
Application of deep reinforcement learning to intrusion detection for supervised problems
M Lopez-Martin, B Carro… - Expert Systems with …, 2020 - Elsevier
The application of new techniques to increase the performance of intrusion detection
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …
systems is crucial in modern data networks with a growing threat of cyber-attacks. These …
An optimized CNN-based intrusion detection system for reducing risks in smart farming
Smart farming is a well-known and superior method of managing a farm, becoming more
prevalent in today's contemporary agricultural practices. Crops are monitored for their …
prevalent in today's contemporary agricultural practices. Crops are monitored for their …
GAN augmentation to deal with imbalance in imaging-based intrusion detection
Nowadays attacks on computer networks continue to advance at a rate outpacing cyber
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
defenders' ability to write new attack signatures. This paper illustrates a deep learning …
Intrusion detection in internet of things using supervised machine learning based on application and transport layer features using UNSW-NB15 data-set
Abstract Internet of Things (IoT) devices are well-connected; they generate and consume
data which involves transmission of data back and forth among various devices. Ensuring …
data which involves transmission of data back and forth among various devices. Ensuring …
[HTML][HTML] Supervised contrastive learning over prototype-label embeddings for network intrusion detection
Contrastive learning makes it possible to establish similarities between samples by
comparing their distances in an intermediate representation space (embedding space) and …
comparing their distances in an intermediate representation space (embedding space) and …
Nearest cluster-based intrusion detection through convolutional neural networks
The recent boom in deep learning has revealed that the application of deep neural networks
is a valuable way to address network intrusion detection problems. This paper presents a …
is a valuable way to address network intrusion detection problems. This paper presents a …
Multi-channel deep feature learning for intrusion detection
Networks had an increasing impact on modern life since network cybersecurity has become
an important research field. Several machine learning techniques have been developed to …
an important research field. Several machine learning techniques have been developed to …