<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The evolving cyber-attack landscape demands flexible and precise protection for information and networks. Network anomaly detection (NAD) systems play a crucial role in preventing and detecting abnormal activities on the network that may lead to catastrophic outcomes when undetected. This paper aims to provide a comprehensive analysis of NAD using unsupervised learning (UL) methods to evaluate the effectiveness of such systems. The paper presents a detailed overview of several UL techniques, lists the current developments and innovations in UL techniques for network anomaly and intrusion detection, and evaluates 13 unsupervised anomaly detection algorithms empirically on benchmark datasets such as NSL-KDD, UNSW-NB15, and CIC-IDS 2017 to analyze the performance of different classes of UL approaches for NAD systems. This study demonstrates the effectiveness of NAD algorithms, discusses UL approaches' research challenges, and unearths the potential drawbacks in the current network security environment.</p>
</abstract>

The evolving cyber-attack landscape demands flexible and precise protection for information and networks. Network anomaly detection (NAD) systems play a crucial role in preventing and detecting abnormal activities on the network that may lead to catastrophic outcomes when undetected. This paper aims to provide a comprehensive analysis of NAD using unsupervised learning (UL) methods to evaluate the effectiveness of such systems. The paper presents a detailed overview of several UL techniques, lists the current developments and innovations in UL techniques for network anomaly and intrusion detection, and evaluates 13 unsupervised anomaly detection algorithms empirically on benchmark datasets such as NSL-KDD, UNSW-NB15, and CIC-IDS 2017 to analyze the performance of different classes of UL approaches for NAD systems. This study demonstrates the effectiveness of NAD algorithms, discusses UL approaches' research challenges, and unearths the potential drawbacks in the current network security environment.

A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection

Department of Computer Science and Information Technology

Department of Computer Science, College of Computer Engineering and Sciences

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The evolving cyber-attack landscape demands flexible and precise protection for information and networks. Network anomaly detection (NAD) systems play a...

A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection

Article Category: Article

Publié en ligne: 19 mai 2024

Pages: -

Reçu: 24 nov. 2023

DOI: https://doi.org/10.2478/ijssis-2024-0016

Mots clés
unsupervised learning, machine learning, unsupervised anomaly detection techniques, network anomaly detection, intrusion detection

© 2024 Niharika Sharma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection

Article Category: Article

Publié en ligne: 19 mai 2024

Pages: -

Reçu: 24 nov. 2023

DOI: https://doi.org/10.2478/ijssis-2024-0016

Mots clésunsupervised learning, machine learning, unsupervised anomaly detection techniques, network anomaly detection, intrusion detection

© 2024 Niharika Sharma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mots clés
unsupervised learning, machine learning, unsupervised anomaly detection techniques, network anomaly detection, intrusion detection