Machine learning models have made many decision support systems to be faster, more
accurate, and more efficient. However, applications of machine learning in network security …

In recent years, machine learning algorithms, and more specifically deep learning
algorithms, have been widely used in many fields, including cyber security. However …

Modern machine learning algorithms are often susceptible to adversarial examples—
maliciously crafted inputs that are undetectable by humans but that fool the algorithm into …

To accelerate research on adversarial examples and robustness of machine learning
classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers …

Machine learning (ML), especially deep learning (DL) techniques have been increasingly
used in anomaly-based network intrusion detection systems (NIDS). However, ML/DL has …

Machine-learning solutions are successfully adopted in multiple contexts but the application
of these techniques to the cyber security domain is complex and still immature. Among the …

Nowadays, intrusion detection systems based on deep learning deliver state-of-the-art
performance. However, recent research has shown that specially crafted perturbations …

With the growth of adversarial attacks against machine learning models, several concerns
have emerged about potential vulnerabilities in designing deep neural network-based …

Machine learning algorithms are widely utilized in cybersecurity. However, recent studies
show that machine learning algorithms are vulnerable to adversarial examples. This poses …

Recent work has shown that deep learning (DL) techniques are highly effective for assisting
network intrusion detection systems (NIDS) in identifying malicious attacks on networks …