Intrusion Detection System Using Data Stream Classification
| Autor: | Mahmood Khalel Ibrahem, Amer Abdulmajeed Abdualrahman |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Data stream
General Computer Science Computer science Computation Feature selection 02 engineering and technology General Chemistry Intrusion detection system computer.software_genre General Biochemistry Genetics and Molecular Biology Field (computer science) Intrusion 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Network intrusion detection computer |
| Zdroj: | Iraqi Journal of Science. :319-328 |
| ISSN: | 2312-1637 0067-2904 |
| DOI: | 10.24996/ijs.2021.62.1.30 |
| Popis: | Secure data communication across networks is always threatened with intrusion and abuse. Network Intrusion Detection System (IDS) is a valuable tool for in-depth defense of computer networks. Most research and applications in the field of intrusion detection systems was built based on analysing the several datasets that contain the attacks types using the classification of batch learning machine. The present study presents the intrusion detection system based on Data Stream Classification. Several data stream algorithms were applied on CICIDS2017 datasets which contain several new types of attacks. The results were evaluated to choose the best algorithm that satisfies high accuracy and low computation time. |
| Databáze: | OpenAIRE |
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