| Autor: |
Pooja Rana, Isha Batra, Arun Malik, In-Ho Ra, Oh-Sung Lee, A. S. M. Sanwar Hosen |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 12, Pp 99223-99239 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3424528 |
| Popis: |
Rife acceptance of Cloud Computing has made it bull’s eye for the hackers. Intrusion detection System (IDS) plays a vibrant role for it. Researchers have done marvelous works on the development of a competence IDS. But there are many challenges still exists with IDS. One of the biggest concerns is that the computational complexity and false alarms of the IDS escalates with the increase in the number of features or attributes of the dataset. Hence, the concept of Feature Selection (FS) contributes an all-important role for the buildout of an efficacious IDS. New FS algorithm is put forward which is the modified Firefly Algorithm in which Decision Tree (DT) classifier is used as the classification function. We have used the hybrid classifier which is the combination of neural network and DT. We have used CSE CIC IDS 2018 dataset and simulated dataset for performance assessment. Our examination pragmatic that the performance of proposed architecture is better than the state-of-the-art algorithms. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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