
Indexed in
License and use
Analysis of institutional authors
Del Rio, ACorresponding AuthorSerrano, JAuthorJimenez, DAuthorLlorente, AAuthorMachine Learning-Based Network Anomaly Detection: Design, Implementation, and Evaluation
Publicated to:Ai. 5 (4): 2967-2983 - 2024-12-01 5(4), DOI: 10.3390/ai5040143
Authors: Schummer, P; del Rio, A; Serrano, J; Jimenez, D; Sánchez, G; Llorente, A
Affiliations
Abstract
Background: In the last decade, numerous methods have been proposed to define and detect outliers, particularly in complex environments like networks, where anomalies significantly deviate from normal patterns. Although defining a clear standard is challenging, anomaly detection systems have become essential for network administrators to efficiently identify and resolve irregularities. Methods: This study develops and evaluates a machine learning-based system for network anomaly detection, focusing on point anomalies within network traffic. It employs both unsupervised and supervised learning techniques, including change point detection, clustering, and classification models, to identify anomalies. SHAP values are utilized to enhance model interpretability. Results: Unsupervised models effectively captured temporal patterns, while supervised models, particularly Random Forest (94.3%), demonstrated high accuracy in classifying anomalies, closely approximating the actual anomaly rate. Conclusions: Experimental results indicate that the system can accurately predict network anomalies in advance. Congestion and packet loss were identified as key factors in anomaly detection. This study demonstrates the potential for real-world deployment of the anomaly detection system to validate its scalability.
Keywords
Quality index
Bibliometric impact. Analysis of the contribution and dissemination channel
The work has been published in the journal Ai due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 86/197, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Computer Science, Artificial Intelligence. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Artificial Intelligence.
Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.
Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-10:
- Scopus: 4
Impact and social visibility
Leadership analysis of institutional authors
There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Schummer, P) and Last Author (LLORENTE GOMEZ, ALVARO).
the author responsible for correspondence tasks has been DEL RIO PONCE, ALBERTO.