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 …

In recent years, advancements in the field of the artificial intelligence (AI) gained a huge
momentum due to the worldwide appliance of this technology by the industry. One of the …

Intrusion Detection and the ability to detect attacks is a crucial aspect to ensure
cybersecurity. However, what if an IDS (Intrusion Detection System) itself is attacked; in other …

Deep neural networks have demonstrated their effectiveness in most machine learning
tasks, with intrusion detection included. Unfortunately, recent research found that deep …

Artificial intelligence algorithms have a leading role in the field of cybersecurity and attack
detection, being able to present better results in some scenarios than classic intrusion …

Traditional approaches in network intrusion detection follow a signature-based approach,
however the use of anomaly detection approaches based on machine learning techniques …

The use of Machine Learning (ML) techniques in Intrusion Detection Systems (IDS) has
taken a prominent role in the network security management field, due to the substantial …

Generative Adversarial Networks (GANs) have seen significant interest since their
introduction in 2014. While originally focused primarily on image-based tasks, their capacity …

The significant growth in the use of the Internet and the rapid development of network
technologies are associated with an increased risk of network attacks. Network attacks refer …

Adversarial examples are inputs to a machine learning system intentionally crafted by an
attacker to fool the model into producing an incorrect output. These examples have achieved …