loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Davide Agostinello ; Angelo Genovese and Vincenzo Piuri

Affiliation: Università degli studi di Milano, Department of Computer Science, Italy

Keyword(s): Deep Learning, Intrusion Detection System, DDoS.

Abstract: The increasing number of connected devices is fostering a rising frequency of cyber attacks, with Distributed Denial of Service (DDoS) attacks among the most common. To counteract DDoS, companies and large organizations are increasingly deploying anomaly-based Intrusion Detection Systems (IDS), which detect attack patterns by analyzing differences in malicious network traffic against a baseline of legitimate traffic. To differentiate malicious and normal traffic, methods based on artificial intelligence and, in particular, Deep Learning (DL) are being increasingly considered, due to their ability to automatically learn feature representations for the different traffic types, without need of explicit programming or handcrafted feature extraction. In this paper, we propose a novel methodology for simulating an anomaly-based IDS based on adaptive DL by designing multiple DL models working with both binary and multi-label classification on multiple datasets with different degrees of comp lexity. To make the DL models adaptable to different conditions, we consider adaptive architectures obtained by automatically tuning the number of neurons for each situation. Results on publicly-available datasets confirm the validity of our proposed methodology, with DL models adapting to the different conditions by increasing the number of neurons on more complex datasets and achieving the highest accuracy in the binary classification configuration. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.235.172.123

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Agostinello, D.; Genovese, A. and Piuri, V. (2023). Anomaly-Based Intrusion Detection System for DDoS Attack with Deep Learning Techniques. In Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-666-8; ISSN 2184-7711, SciTePress, pages 267-275. DOI: 10.5220/0012146100003555

@conference{secrypt23,
author={Davide Agostinello. and Angelo Genovese. and Vincenzo Piuri.},
title={Anomaly-Based Intrusion Detection System for DDoS Attack with Deep Learning Techniques},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT},
year={2023},
pages={267-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012146100003555},
isbn={978-989-758-666-8},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT
TI - Anomaly-Based Intrusion Detection System for DDoS Attack with Deep Learning Techniques
SN - 978-989-758-666-8
IS - 2184-7711
AU - Agostinello, D.
AU - Genovese, A.
AU - Piuri, V.
PY - 2023
SP - 267
EP - 275
DO - 10.5220/0012146100003555
PB - SciTePress