With the recent increase in internet usage, the number of important, sensitive, confidential
individual and corporate data passing through internet has increasingly grown. With gaps in …

In light of the escalating ubiquity of the Internet, the proliferation of cyber-attacks, coupled
with their intricate and surreptitious nature, has significantly imperiled network security …

Anomaly detection approaches for network intrusion detection learn to identify deviations
from normal behavior on a data-driven basis. However, current approaches strive to infer the …

With the recent rise in internet usage, the volume of crucial, private, and confidential data
traveling online has increased. Attackers have made attempts to break into the network due …

Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent
mechanisms for actively detecting fresh attacks over a complex network. Although review …

This paper thoroughly compares thirteen unique Machine Learning (ML) models utilized for
Intrusion detection systems (IDS) in a meticulously controlled environment. Unlike previous …

In recent years, we have witnessed rapid growth in the application of IoT globally. IoT has
found its applications in governmental and non-governmental institutions. The integration of …

Traffic classification is an automated technique that divides computer network traffic into
several categories depending on different factors like protocol or port number. In a …

To address the challenges posed by high data dimensionality and class imbalance during
intrusion detection, which result in increased computational complexity, resource …

The rise in cyberattacks targeting critical network infrastructure has spurred an increased
emphasis on the development of robust cybersecurity measures. In this context, there is a …