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Authors: Gustavo Gonzalez-Granadillo ; Alejandro G. Bedoya and Rodrigo Diaz

Affiliation: Atos Research & Innovation, Cybersecurity Laboratory, Spain

Keyword(s): Deep Learning, Neural Network, Anomaly Detection, Network Traffic Behavior, AutoEncoder.

Abstract: Network Anomaly detection is an open issue that considers the problem of finding patterns in data that do not conform to expected behavior. Anomalies exhibit themselves in network statistics differently; therefore developing general models of normal network behavior and anomalies is a challenging task. This paper presents an Improved Live Anomaly Detection System (I-LADS) based on AutoEncoder (AE), a well known deep learning algorithm, to detect network traffic anomalies. I-LADS comes in two versions: (i) I-LADS-v1, that uses filters to independently model IP addresses from the NetFlow dataset, making it possible to train one model for each filtered IP address; and (ii) I-LADS-v2, that uses no filter and therefore a single algorithm is trained for all IP addresses. Experiments have been conducted using a valid dataset containing over two million connections to build a model with multiple features in order to identify the approach that most accurately detects traffic anomalies in the target network. Preliminary results show a promising solution with 99% and 94% of accuracy for the supervised and unsupervised learning approaches respectively. (More)

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Paper citation in several formats:
Gonzalez-Granadillo, G., Bedoya, A. G. and Diaz, R. (2021). An Improved Live Anomaly Detection System (I-LADS) based on Deep Learning Algorithms. In Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-524-1; ISSN 2184-7711, SciTePress, pages 568-575. DOI: 10.5220/0010573705680575

@conference{secrypt21,
author={Gustavo Gonzalez{-}Granadillo and Alejandro G. Bedoya and Rodrigo Diaz},
title={An Improved Live Anomaly Detection System (I-LADS) based on Deep Learning Algorithms},
booktitle={Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT},
year={2021},
pages={568-575},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010573705680575},
isbn={978-989-758-524-1},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 18th International Conference on Security and Cryptography - SECRYPT
TI - An Improved Live Anomaly Detection System (I-LADS) based on Deep Learning Algorithms
SN - 978-989-758-524-1
IS - 2184-7711
AU - Gonzalez-Granadillo, G.
AU - Bedoya, A.
AU - Diaz, R.
PY - 2021
SP - 568
EP - 575
DO - 10.5220/0010573705680575
PB - SciTePress

- Science and Technology Publications, Lda.
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