2023
DOI: 10.32604/csse.2023.029984
|View full text |Cite
|
Sign up to set email alerts
|

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Abstract: Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier's parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting i… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 24 publications
0
2
0
Order By: Relevance
“…Sequencing was done at the same place using the Illumina NovaSeq 6000 platform with 2x100bp sequencing read length. The raw sequence data was ltered and assembled using a GitHub pipeline 21 .…”
Section: Methodsmentioning
confidence: 99%
“…Sequencing was done at the same place using the Illumina NovaSeq 6000 platform with 2x100bp sequencing read length. The raw sequence data was ltered and assembled using a GitHub pipeline 21 .…”
Section: Methodsmentioning
confidence: 99%
“…Sequencing was done at the same place using the Illumina NovaSeq 6000 platform with 2x100bp sequencing read length. The raw sequence data was filtered and assembled using a GitHub pipeline 21 .…”
Section: Methodsmentioning
confidence: 99%