Ateeq, K, Anbar, Mohammed, Alashhab, Ziyad R., Rihan, Shaza Dawood Ahmed and Alabsi, Basim Ahmad (2023) Enhancing Cloud Computing Analysis: A CCE-Based HTTP-GET Log Dataset. Applied Sciences, 13 (16). p. 9086. ISSN 2076-3417
13/16/9086 - Published Version
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Abstract
The Hypertext Transfer Protocol (HTTP) is a common target of distributed denial-of-service (DDoS) attacks in today’s cloud computing environment (CCE). However, most existing datasets for Intrusion Detection System (IDS) evaluations are not suitable for CCEs. They are either self-generated or are not representative of CCEs, leading to high false alarm rates when used in real CCEs. Moreover, many datasets are inaccessible due to privacy and copyright issues. Therefore, we propose a publicly available benchmark dataset of HTTP-GET flood DDoS attacks on CCEs based on an actual private CCE. The proposed dataset has two advantages: (1) it uses CCE-based features, and (2) it meets the criteria for trustworthy and valid datasets. These advantages enable reliable IDS evaluations, tuning, and comparisons. Furthermore, the dataset includes both internal and external HTTP-GET flood DDoS attacks on CCEs. This dataset can facilitate research in the field and enhance CCE security against DDoS attacks.
Affiliation: | Skyline University College |
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SUC Author(s): | Ateeq, K ORCID: https://orcid.org/0000-0002-6712-6623 |
All Author(s): | Ateeq, K, Anbar, Mohammed, Alashhab, Ziyad R., Rihan, Shaza Dawood Ahmed and Alabsi, Basim Ahmad |
Item Type: | Article |
Uncontrolled Keywords: | cybersecurity; intrusion detection; dataset generation; cloud computing environment (CCE); distributed denial-of-service (DDoS) attacks; HTTP-GET; flood DDoS attacks; application-layer attacks |
Subjects: | B Information Technology > BV Cloud Computing |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Mosys Team |
Date Deposited: | 25 Dec 2023 13:27 |
Last Modified: | 25 Dec 2023 13:27 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/750 |
Publisher URL: | https://doi.org/10.3390/app13169086 |
Publisher OA policy: | https://v2.sherpa.ac.uk/id/publication/22262 |
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