| Autor: |
Fariha Moomtaheen, Sikha S. Bagui, Subhash C. Bagui, Dustin Mink |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Information, Vol 15, Iss 7, p 404 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2078-2489 |
| DOI: |
10.3390/info15070404 |
| Popis: |
The novelty of this paper is in determining and using hyperparameters to improve the Extended Isolation Forest (EIF) algorithm, a relatively new algorithm, to detect malicious activities in network traffic. The EIF algorithm is a variation of the Isolation Forest algorithm, known for its efficacy in detecting anomalies in high-dimensional data. Our research assesses the performance of the EIF model on a newly created dataset composed of Zeek Connection Logs, UWF-ZeekDataFall22. To handle the enormous volume of data involved in this research, the Hadoop Distributed File System (HDFS) is employed for efficient and fault-tolerant storage, and the Apache Spark framework, a powerful open-source Big Data analytics platform, is utilized for machine learning (ML) tasks. The best results for the EIF algorithm came from the 0-extension level. We received an accuracy of 82.3% for the Resource Development tactic, 82.21% for the Reconnaissance tactic, and 78.3% for the Discovery tactic. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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