A systematic comparison on prevailing intrusion detection models

J Liu, H Xue, J Wang, S Hong, H Fu, O Dib - International Conference on …, 2022 - Springer
Modern vehicles have become connected via On-Board Units (OBUs) involving many
complex embedded and networked devices with steadily increasing processing and …

[PDF][PDF] Incorporating multiple supervised learning algorithms for effective intrusion detection

U Albalawi, SC Suh, J Kim - International Journal of Computer …, 2014 - researchgate.net
As internet continues to expand its usage with an enormous number of applications, cyber-
threats have significantly increased accordingly. Thus, accurate detection of malicious traffic …

A Hybrid Supervised Learning Approach for Intrusion Detection Systems

T Liu, W Fan, G Wang, W Tang, D Li, M Chen… - … on Knowledge and …, 2023 - Springer
The Internet's rapid development has raised significant concerns regarding network attacks'
increasing frequency and evolving nature. Consequently, there is an urgent demand for an …

Evaluating machine learning algorithms for intrusion detection systems using the dataset CIDDS-002

QV Dang - Proceedings of the 4th International Conference on …, 2021 - dl.acm.org
Intrusion detection systems play a vital role in protecting computer systems from external
attacks. In recent years, many open-source intrusion datasets have been released to let …

Machine learning for network-based intrusion detection systems: an analysis of the CIDDS-001 dataset

J Carneiro, N Oliveira, N Sousa, E Maia… - … symposium on distributed …, 2021 - Springer
With the increasing amount of reliance on digital data and computer networks by
corporations and the public in general, the occurrence of cyber attacks has become a great …

Machine Learning Techniques for Network Intrusion Detection

TP Tran, P Tsai, T Jan, X He - Machine Learning: Concepts …, 2012 - igi-global.com
Most of the currently available network security techniques are not able to cope with the
dynamic and increasingly complex nature of cyber attacks on distributed computer systems …

Intrusion detection: supervised machine learning

AH Fares, MI Sharawy, HH Zayed - Journal of Computing Science …, 2011 - koreascience.kr
Due to the expansion of high-speed Internet access, the need for secure and reliable
networks has become more critical. The sophistication of network attacks, as well as their …

Comparative evaluation of machine learning algorithms for network intrusion detection and attack classification

M Leon, T Markovic, S Punnekkat - 2022 international joint …, 2022 - ieeexplore.ieee.org
With the increasing use of the internet and reliance on computer-based systems for our daily
lives, any vulnerability in those systems is one of the most important issues for the …

Machine Learning on Public Intrusion Datasets: Academic Hype or Concrete Advances in NIDS?

M Catillo, A Pecchia, U Villano - 2023 53rd Annual IEEE/IFIP …, 2023 - ieeexplore.ieee.org
The number of papers on network intrusion detection based on machine and deep learning
is growing at an unprecedented rate. Most of these papers follow a well-consolidated …

A Comparative Analysis of Machine and Deep Learning Classifiers for Intrusion Detection

PLS Jayalaxmi, R Saha… - 2023 5th International …, 2023 - ieeexplore.ieee.org
Presently available security tools are eluded by polymorphic malware, zero-day
vulnerabilities, and unauthorized attempts. These open up loophole points in the systems for …