To deal with the increasing number of cyber-attacks, intrusion detection system (IDS) plays
an important role in monitoring and ensuring the security of the computer network. With the …

Traditional approaches to cybersecurity issues usually protect users from attacks after the
occurrence of specific types of attacks. Besides, patterns of recent cyberattacks tend to be …

Existing machine learning solutions for network-based intrusion detection cannot maintain
their reliability over time when facing high-speed networks and evolving attacks. In this …

Federated learning (FL) has become an increasingly popular solution for intrusion detection
to avoid data privacy leakage in Internet of Things (IoT) edge devices. However, most of the …

Network intrusion detection system (NIDS) adoption is essential for mitigating computer
network attacks in various scenarios. However, the increasing complexity of computer …

Through the ongoing digitization of the world, the number of connected devices is
continuously growing without any foreseen decline in the near future. In particular, these …

In the modern interconnected world, intelligent networks and computing technologies are
increasingly being incorporated in industrial systems. However, this adoption of advanced …

In this paper, a new learning algorithm for adaptive network intrusion detection using naive
Bayesian classifier and decision tree is presented, which performs balance detections and …

Cyberattacks are increasingly threatening net-worked systems, often with the emergence of
new types of unknown (zero-day) attacks and the rise of vulnerable devices. uch attacks can …

With the advantage of analyzing data of multiple work sites comprehensively while ensuring
data privacy, federated learning-based intrusion detection systems (IDS) are emerging as a …