Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders

2025/06/17 by Luan Gonçalves Miranda, Miranda, Luan Gonçalves, Pedro Cruz +3
Computer Science · #Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Network Security and Intrusion Detection #Networking and Internet Architecture (cs.NI) #Performance (cs.PF)

paper · pdf · doi:10.48550/arxiv.2506.14937

openalex publication_date 2025/06/17 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

Abstract

Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders (AE) show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction error, the definition of this threshold directly impacts the performance of the detection process. Thus, this work proposes the automatic definition of this threshold using some machine learning algorithms. For this, three algorithms were evaluated: the K-Nearst Neighbors, the K-Means and the Support Vector Machine.

Citations

• Detecting network anomalies using machine learning and SNMP-MIB dataset with IP group
• Theoretical Analysis of the k-Means Algorithm - A Survey

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