Multi-Stage Intrusion Detection System aided by Grey Wolf optimization algorithm Article Swipe
Somnath Chatterjee
,
Vaibhav Shaw
,
Ranit Das
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2680915/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2680915/v1
A Network Intrusion Detection System (NIDS) is frequently used for monitoring and detecting malicious activities in network traffic. A typical NIDS has four stages: a data source, data pre-processing, a decision-making technique, and a defense reaction. We have utilized both anomaly and signature based techniques to build a framework which is resilient to identifying both known and unknown attack. The incoming data packet is fed into the Stacked Autoencoder to identify whether it is a benign or malicious. If found to be malicious we extract the most relevant features from the network packet using Grey Wolf Optimization algorithm. Then these attribute are provided to RandomForest classifier to determine if this malign attack is present in our knowledge base. If it is present we progress to identify the attack type using LightGBM classifier. If not, we term it as zero-day attack. To evaluate the usability of the proposed framework we have assessed it using two publicly available datasets namely UNSW-NB15 and CIC-IDS-2017 dataset. We have obtained an accuracy of 90.94% and 99.67% on the datasets respectively.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2680915/v1
- https://www.researchsquare.com/article/rs-2680915/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4324378829
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4324378829Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2680915/v1Digital Object Identifier
- Title
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Multi-Stage Intrusion Detection System aided by Grey Wolf optimization algorithmWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-03-15Full publication date if available
- Authors
-
Somnath Chatterjee, Vaibhav Shaw, Ranit DasList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2680915/v1Publisher landing page
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https://www.researchsquare.com/article/rs-2680915/latest.pdfDirect link to full text PDF
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YesWhether a free full text is available
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-
greenOpen access status per OpenAlex
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https://www.researchsquare.com/article/rs-2680915/latest.pdfDirect OA link when available
- Concepts
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Computer science, Intrusion detection system, Classifier (UML), Autoencoder, Data mining, Network packet, Usability, Machine learning, Artificial intelligence, Pattern recognition (psychology), Artificial neural network, Computer security, Human–computer interactionTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.extract | 85 |
| abstract_inverted_index.network | 17, 92 |
| abstract_inverted_index.present | 114, 122 |
| abstract_inverted_index.source, | 27 |
| abstract_inverted_index.stages: | 24 |
| abstract_inverted_index.typical | 20 |
| abstract_inverted_index.unknown | 58 |
| abstract_inverted_index.whether | 72 |
| abstract_inverted_index.LightGBM | 131 |
| abstract_inverted_index.accuracy | 167 |
| abstract_inverted_index.assessed | 151 |
| abstract_inverted_index.dataset. | 162 |
| abstract_inverted_index.datasets | 157, 174 |
| abstract_inverted_index.evaluate | 142 |
| abstract_inverted_index.features | 89 |
| abstract_inverted_index.identify | 71, 126 |
| abstract_inverted_index.incoming | 61 |
| abstract_inverted_index.obtained | 165 |
| abstract_inverted_index.progress | 124 |
| abstract_inverted_index.proposed | 147 |
| abstract_inverted_index.provided | 103 |
| abstract_inverted_index.publicly | 155 |
| abstract_inverted_index.relevant | 88 |
| abstract_inverted_index.traffic. | 18 |
| abstract_inverted_index.utilized | 39 |
| abstract_inverted_index.zero-day | 139 |
| abstract_inverted_index.Detection | 4 |
| abstract_inverted_index.Intrusion | 3 |
| abstract_inverted_index.UNSW-NB15 | 159 |
| abstract_inverted_index.attribute | 101 |
| abstract_inverted_index.available | 156 |
| abstract_inverted_index.detecting | 13 |
| abstract_inverted_index.determine | 108 |
| abstract_inverted_index.framework | 49, 148 |
| abstract_inverted_index.knowledge | 117 |
| abstract_inverted_index.malicious | 14, 83 |
| abstract_inverted_index.reaction. | 36 |
| abstract_inverted_index.resilient | 52 |
| abstract_inverted_index.signature | 43 |
| abstract_inverted_index.usability | 144 |
| abstract_inverted_index.activities | 15 |
| abstract_inverted_index.algorithm. | 98 |
| abstract_inverted_index.classifier | 106 |
| abstract_inverted_index.frequently | 8 |
| abstract_inverted_index.malicious. | 78 |
| abstract_inverted_index.monitoring | 11 |
| abstract_inverted_index.technique, | 32 |
| abstract_inverted_index.techniques | 45 |
| abstract_inverted_index.Autoencoder | 69 |
| abstract_inverted_index.classifier. | 132 |
| abstract_inverted_index.identifying | 54 |
| abstract_inverted_index.CIC-IDS-2017 | 161 |
| abstract_inverted_index.Optimization | 97 |
| abstract_inverted_index.RandomForest | 105 |
| abstract_inverted_index.respectively. | 175 |
| abstract_inverted_index.decision-making | 31 |
| abstract_inverted_index.pre-processing, | 29 |
| abstract_inverted_index.<title>Abstract</title> | 0 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.47315786 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |