Toward generating a DoS and scan statistical network traffic metrics for building intrusion detection solution based on machine and deep learning: I-Sec-IDS datasets

Conférence Chapitre d'actes Toward generating a DoS and scan statistical network traffic metrics for building intrusion detection solution based on machine and deep learning: I-Sec-IDS datasets eng 2023 author Serinelli Benedetto Marco 928920 10.1063/5.0141860 We propose a Denial of Service (DoS) and scan statistical network traffic metrics datasets to build upon Intrusion Detection System (IDS) solutions built on Machine and Deep Learning (MDL) methodologies. We generate the datasets in VirtualBox environment. Two guests are involved and configured to perform the aforementioned attacks for collecting the network traffic. The first one is a Kali Linux VirtualBox machine that executes the DoS and scan attacks against the second one guest, a Microsoft 10. The host machine captures the exchanged network traffic between two guests via Wireshark and saves it in PCAP files. We extract the Canadian Institute of Cybersecurity (CIC)’ FlowMeter metrics in Comma-separated Values (CSV) format to label our generated statistical network traffic metrics. Thus, this paper produces the DoS and scan statistical network traffic metrics datasets, cleansed up them before to be free available. To conclude, this work realises a first training datasets estimation to design an IDS solutions, based on MDL techniques, upon our datasets. Published in: AIP Conf. Proc. 2724, 040010 (2023) Dewey 025.063 13 Centre universitaire d'informatique 0D3 false Computational intelligence and network security 3–4 March 2022 Raipur, C.G. (India) ba007279-8ee8-47b7-b252-b205f0bf55d5 Proceedings chapter (Published version) Restricted 040010_1_5.01418601.pdf 365943 application/pdf bc2b5fc0a396b710039db36069d69359fb0768399ab0753b00e74938ec3081f4 restrictedAccess Centres et instituts / Centre universitaire d'informatique

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Toward generating a DoS and scan statistical network traffic metrics for building intrusion detection solution based on machine and deep learning: I-Sec-IDS datasets	Computational intelligence and network security			2023	3	0

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Serinelli, Benedetto Marco