The number of papers on machine learning and deep neural networks applied to intrusion
detection systems (IDS) is ever-increasing. Differently from existing work on the topic, this …

With the rapid development of Industrial Internet, the network intrusion detection has
become particularly important. In the Industrial Internet, large-scale data is distributed in the …

The signature-based network intrusion detection systems (IDSs) entail relying on a pre-
established signatures and IP addresses that are frequently updated to keep up with the …

Intrusion detection systems (IDS) play a vital role in protecting networks from malicious
attacks. Modern IDS use machine-learning or deep-learning models to deal with the …

With the advancement of Machine Learning (ML) algorithms, more organizations started
using Machine Learning based Intrusion Detection Systems (ML-IDSs) to mitigate …

Abstract Internet of Things (IoT) devices are extensively utilized but are susceptible to
cyberattacks, posing significant security challenges. To mitigate these threats, machine …

The significant rise of security concerns in conventional centralized learning has promoted
federated learning (FL) adoption in building intelligent applications without privacy …

With the developing technology, the need for the dissemination and protection of information
is becoming increasingly important. Recently, attacks on information systems have …

In the current interconnected and susceptible digital environment, the demand for reliable
network attack prediction systems has reached a critical level. This research paper presents …

Feature selection is a vital preprocessing step before utilizing any machine learning
algorithm. It aims at reducing the number of features in the dataset by removing irrelevant …