Výsledky vyhledávání - "PEKÁR, Adrián"

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AutoFlow: An Autoencoder-based Approach for IP Flow Record Compression with Minimal Impact on Traffic Classification

Autor: Pekar, Adrian

This paper presents a novel approach to compressing IP flow records using deep learning techniques, specifically autoencoders. Our method aims to significantly reduce data volume while maintaining the utility of the compressed data for downstream ana

Externí odkaz: http://arxiv.org/abs/2410.00030

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Early Detection of Network Service Degradation: An Intra-Flow Approach

Autor: Bicski, Balint, Pekar, Adrian

This research presents a novel method for predicting service degradation (SD) in computer networks by leveraging early flow features. Our approach focuses on the observable (O) segments of network flows, particularly analyzing Packet Inter-Arrival Ti

Externí odkaz: http://arxiv.org/abs/2407.06637

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Early-Stage Anomaly Detection: A Study of Model Performance on Complete vs. Partial Flows

Autor: Pekar, Adrian, Jozsa, Richard

This study investigates the efficacy of machine learning models, specifically Random Forest, in anomaly detection systems when trained on complete flow records and tested on partial flow data. We explore the performance disparity that arises when mod

Externí odkaz: http://arxiv.org/abs/2407.02856

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Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study

Autor: Pekar, Adrian, Jozsa, Richard

Cybersecurity remains a critical challenge in the digital age, with network traffic flow anomaly detection being a key pivotal instrument in the fight against cyber threats. In this study, we address the prevalent issue of data integrity in network t

Externí odkaz: http://arxiv.org/abs/2401.16843

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