Mechanism for Extracting Features Using Particle Swarm Optimization for Intrusion Detection Systems Article Swipe
Vaishnavi Sivagaminathan
,
Manmohan Sharma
,
Santosh Kumar Henge
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2429488/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2429488/v1
The task of ensuring cyber-security has grown increasingly challenging given the concerning expansion of Computing connection and furthermore, there are a large number of computer-related applications available. It also needs a strong defense mechanism towards various cyber-attacks. Identifying irregularities and dangers in such a computer Security measures (IDS) have been established to aid with information security. Particularly, ML approaches are a subset of artificial intelligence (ai). (AI), a useful data-driven anti - malware system was developed. Two alternative intrusion detection (ID) classification reaches were compared in this study, each with its own set of use cases. Before using the two classifiers for classification, the Particle Swarm Optimization (PSO) approach ware used for reduce dimensionality. The classification meets used to characterize network anomalies were studied in this study. PSO + ANN (Artificial neural network), PSO plus Decision Tree and PSO plus K-Nearest Neighbor are the three classifiers used. The Knowledge discovery in databases 99 datasets was used to corroborate the identification techniques' findings. On the result of the implementation, successful metrics like as the following metrics were used to analyze cyber-security databases for various kinds of cyber-attacks: specific, recall, f1-score, correctness, accuracy, and constancy. The two's respective precision, detection rate (DR), and totally bogus rate were also compared to see which one outperforms the other (FPR). The solution was then contrasted with the IDS that was already in place. In terms of detecting network anomalies, The outcomes show that PSO + ANN outperforms the PSO + KNN and PSO + DT classifier algorithms.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2429488/v1
- https://www.researchsquare.com/article/rs-2429488/latest.pdf
- OA Status
- green
- References
- 50
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313553561
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4313553561Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-2429488/v1Digital Object Identifier
- Title
-
Mechanism for Extracting Features Using Particle Swarm Optimization for Intrusion Detection SystemsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-05Full publication date if available
- Authors
-
Vaishnavi Sivagaminathan, Manmohan Sharma, Santosh Kumar HengeList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2429488/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-2429488/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-2429488/latest.pdfDirect OA link when available
- Concepts
-
Computer science, Particle swarm optimization, Intrusion detection system, Artificial intelligence, Classifier (UML), Correctness, Data mining, Machine learning, Artificial neural network, Curse of dimensionality, Network security, Computer security, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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50Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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