An Intrusion Detection Algorithm for DDoS Attacks Based on DBN and Three-way Decisions Article Swipe
To solve the problems of few DDoS attack detection methods and low intrusion detection rate of existing methods in software defined network (SDN), an intrusion detection algorithm DBN-TWD based on deep belief network (DBN) and Three-way decisions was proposed. Firstly, DBN was used to extract features of SDN flow entries, then directly classifying data in the positive and negative domains, and the data in the boundary domain is reclassified by the K-nearest neighbor algorithm. Simulation results show that compared with other intrusion detection models, the detection rate of this method is higher, and the false alarm rate is lower.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1742-6596/2356/1/012044
- OA Status
- diamond
- Cited By
- 11
- References
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4306787212
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4306787212Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1088/1742-6596/2356/1/012044Digital Object Identifier
- Title
-
An Intrusion Detection Algorithm for DDoS Attacks Based on DBN and Three-way DecisionsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-01Full publication date if available
- Authors
-
Yanjie ShenList of authors in order
- Landing page
-
https://doi.org/10.1088/1742-6596/2356/1/012044Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1088/1742-6596/2356/1/012044Direct OA link when available
- Concepts
-
Computer science, Intrusion detection system, Constant false alarm rate, Denial-of-service attack, False positive rate, Deep belief network, Data mining, Artificial intelligence, Algorithm, The Internet, Deep learning, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 7, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
3Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4306787212 |
|---|---|
| doi | https://doi.org/10.1088/1742-6596/2356/1/012044 |
| ids.doi | https://doi.org/10.1088/1742-6596/2356/1/012044 |
| ids.openalex | https://openalex.org/W4306787212 |
| fwci | 2.35665919 |
| type | article |
| title | An Intrusion Detection Algorithm for DDoS Attacks Based on DBN and Three-way Decisions |
| biblio.issue | 1 |
| biblio.volume | 2356 |
| biblio.last_page | 012044 |
| biblio.first_page | 012044 |
| topics[0].id | https://openalex.org/T10400 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1705 |
| topics[0].subfield.display_name | Computer Networks and Communications |
| topics[0].display_name | Network Security and Intrusion Detection |
| topics[1].id | https://openalex.org/T11241 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.994700014591217 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1711 |
| topics[1].subfield.display_name | Signal Processing |
| topics[1].display_name | Advanced Malware Detection Techniques |
| topics[2].id | https://openalex.org/T10714 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9909999966621399 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1705 |
| topics[2].subfield.display_name | Computer Networks and Communications |
| topics[2].display_name | Software-Defined Networks and 5G |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C41008148 |
| concepts[0].level | 0 |
| concepts[0].score | 0.764606237411499 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[0].display_name | Computer science |
| concepts[1].id | https://openalex.org/C35525427 |
| concepts[1].level | 2 |
| concepts[1].score | 0.7412672638893127 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q745881 |
| concepts[1].display_name | Intrusion detection system |
| concepts[2].id | https://openalex.org/C77052588 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6752394437789917 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q644307 |
| concepts[2].display_name | Constant false alarm rate |
| concepts[3].id | https://openalex.org/C38822068 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6480801105499268 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q131406 |
| concepts[3].display_name | Denial-of-service attack |
| concepts[4].id | https://openalex.org/C95922358 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5496984720230103 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q5432725 |
| concepts[4].display_name | False positive rate |
| concepts[5].id | https://openalex.org/C97385483 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5460219383239746 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q16954980 |
| concepts[5].display_name | Deep belief network |
| concepts[6].id | https://openalex.org/C124101348 |
| concepts[6].level | 1 |
| concepts[6].score | 0.4452536702156067 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[6].display_name | Data mining |
| concepts[7].id | https://openalex.org/C154945302 |
| concepts[7].level | 1 |
| concepts[7].score | 0.43974217772483826 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[7].display_name | Artificial intelligence |
| concepts[8].id | https://openalex.org/C11413529 |
| concepts[8].level | 1 |
| concepts[8].score | 0.34673506021499634 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[8].display_name | Algorithm |
| concepts[9].id | https://openalex.org/C110875604 |
| concepts[9].level | 2 |
| concepts[9].score | 0.1699855923652649 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q75 |
| concepts[9].display_name | The Internet |
| concepts[10].id | https://openalex.org/C108583219 |
| concepts[10].level | 2 |
| concepts[10].score | 0.14124366641044617 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[10].display_name | Deep learning |
| concepts[11].id | https://openalex.org/C136764020 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[11].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/computer-science |
| keywords[0].score | 0.764606237411499 |
| keywords[0].display_name | Computer science |
| keywords[1].id | https://openalex.org/keywords/intrusion-detection-system |
| keywords[1].score | 0.7412672638893127 |
| keywords[1].display_name | Intrusion detection system |
| keywords[2].id | https://openalex.org/keywords/constant-false-alarm-rate |
| keywords[2].score | 0.6752394437789917 |
| keywords[2].display_name | Constant false alarm rate |
| keywords[3].id | https://openalex.org/keywords/denial-of-service-attack |
| keywords[3].score | 0.6480801105499268 |
| keywords[3].display_name | Denial-of-service attack |
| keywords[4].id | https://openalex.org/keywords/false-positive-rate |
| keywords[4].score | 0.5496984720230103 |
| keywords[4].display_name | False positive rate |
| keywords[5].id | https://openalex.org/keywords/deep-belief-network |
| keywords[5].score | 0.5460219383239746 |
| keywords[5].display_name | Deep belief network |
| keywords[6].id | https://openalex.org/keywords/data-mining |
| keywords[6].score | 0.4452536702156067 |
| keywords[6].display_name | Data mining |
| keywords[7].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[7].score | 0.43974217772483826 |
| keywords[7].display_name | Artificial intelligence |
| keywords[8].id | https://openalex.org/keywords/algorithm |
| keywords[8].score | 0.34673506021499634 |
| keywords[8].display_name | Algorithm |
| keywords[9].id | https://openalex.org/keywords/the-internet |
| keywords[9].score | 0.1699855923652649 |
| keywords[9].display_name | The Internet |
| keywords[10].id | https://openalex.org/keywords/deep-learning |
| keywords[10].score | 0.14124366641044617 |
| keywords[10].display_name | Deep learning |
| language | en |
| locations[0].id | doi:10.1088/1742-6596/2356/1/012044 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210187594 |
| locations[0].source.issn | 1742-6588, 1742-6596 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1742-6588 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Journal of Physics Conference Series |
| locations[0].source.host_organization | https://openalex.org/P4310320083 |
| locations[0].source.host_organization_name | IOP Publishing |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| locations[0].source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Journal of Physics: Conference Series |
| locations[0].landing_page_url | https://doi.org/10.1088/1742-6596/2356/1/012044 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5036135848 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6880-4705 |
| authorships[0].author.display_name | Yanjie Shen |
| authorships[0].countries | RU |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I212220629 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia |
| authorships[0].institutions[0].id | https://openalex.org/I212220629 |
| authorships[0].institutions[0].ror | https://ror.org/02x91aj62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I212220629 |
| authorships[0].institutions[0].country_code | RU |
| authorships[0].institutions[0].display_name | Peter the Great St. Petersburg Polytechnic University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Yanjie Shen |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Institute of Computer Science and Technology, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1088/1742-6596/2356/1/012044 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | An Intrusion Detection Algorithm for DDoS Attacks Based on DBN and Three-way Decisions |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10400 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1705 |
| primary_topic.subfield.display_name | Computer Networks and Communications |
| primary_topic.display_name | Network Security and Intrusion Detection |
| related_works | https://openalex.org/W3093744786, https://openalex.org/W224405725, https://openalex.org/W2393267898, https://openalex.org/W4205383432, https://openalex.org/W2369874171, https://openalex.org/W2013909972, https://openalex.org/W2383301100, https://openalex.org/W2619636815, https://openalex.org/W2392864074, https://openalex.org/W2352639800 |
| cited_by_count | 11 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 7 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 2 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1088/1742-6596/2356/1/012044 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210187594 |
| best_oa_location.source.issn | 1742-6588, 1742-6596 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1742-6588 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Journal of Physics Conference Series |
| best_oa_location.source.host_organization | https://openalex.org/P4310320083 |
| best_oa_location.source.host_organization_name | IOP Publishing |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| best_oa_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Journal of Physics: Conference Series |
| best_oa_location.landing_page_url | https://doi.org/10.1088/1742-6596/2356/1/012044 |
| primary_location.id | doi:10.1088/1742-6596/2356/1/012044 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210187594 |
| primary_location.source.issn | 1742-6588, 1742-6596 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1742-6588 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Journal of Physics Conference Series |
| primary_location.source.host_organization | https://openalex.org/P4310320083 |
| primary_location.source.host_organization_name | IOP Publishing |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320083, https://openalex.org/P4310311669 |
| primary_location.source.host_organization_lineage_names | IOP Publishing, Institute of Physics |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Journal of Physics: Conference Series |
| primary_location.landing_page_url | https://doi.org/10.1088/1742-6596/2356/1/012044 |
| publication_date | 2022-10-01 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W2783146946, https://openalex.org/W2787467369, https://openalex.org/W2908941882 |
| referenced_works_count | 3 |
| abstract_inverted_index.To | 0 |
| abstract_inverted_index.an | 23 |
| abstract_inverted_index.by | 69 |
| abstract_inverted_index.in | 18, 54, 63 |
| abstract_inverted_index.is | 67, 90, 97 |
| abstract_inverted_index.of | 4, 15, 46, 87 |
| abstract_inverted_index.on | 29 |
| abstract_inverted_index.to | 43 |
| abstract_inverted_index.DBN | 40 |
| abstract_inverted_index.SDN | 47 |
| abstract_inverted_index.and | 10, 34, 57, 60, 92 |
| abstract_inverted_index.few | 5 |
| abstract_inverted_index.low | 11 |
| abstract_inverted_index.the | 2, 55, 61, 64, 70, 84, 93 |
| abstract_inverted_index.was | 37, 41 |
| abstract_inverted_index.DDoS | 6 |
| abstract_inverted_index.data | 53, 62 |
| abstract_inverted_index.deep | 30 |
| abstract_inverted_index.flow | 48 |
| abstract_inverted_index.rate | 14, 86, 96 |
| abstract_inverted_index.show | 76 |
| abstract_inverted_index.that | 77 |
| abstract_inverted_index.then | 50 |
| abstract_inverted_index.this | 88 |
| abstract_inverted_index.used | 42 |
| abstract_inverted_index.with | 79 |
| abstract_inverted_index.(DBN) | 33 |
| abstract_inverted_index.alarm | 95 |
| abstract_inverted_index.based | 28 |
| abstract_inverted_index.false | 94 |
| abstract_inverted_index.other | 80 |
| abstract_inverted_index.solve | 1 |
| abstract_inverted_index.(SDN), | 22 |
| abstract_inverted_index.attack | 7 |
| abstract_inverted_index.belief | 31 |
| abstract_inverted_index.domain | 66 |
| abstract_inverted_index.lower. | 98 |
| abstract_inverted_index.method | 89 |
| abstract_inverted_index.DBN-TWD | 27 |
| abstract_inverted_index.defined | 20 |
| abstract_inverted_index.extract | 44 |
| abstract_inverted_index.higher, | 91 |
| abstract_inverted_index.methods | 9, 17 |
| abstract_inverted_index.models, | 83 |
| abstract_inverted_index.network | 21, 32 |
| abstract_inverted_index.results | 75 |
| abstract_inverted_index.Firstly, | 39 |
| abstract_inverted_index.boundary | 65 |
| abstract_inverted_index.compared | 78 |
| abstract_inverted_index.directly | 51 |
| abstract_inverted_index.domains, | 59 |
| abstract_inverted_index.entries, | 49 |
| abstract_inverted_index.existing | 16 |
| abstract_inverted_index.features | 45 |
| abstract_inverted_index.negative | 58 |
| abstract_inverted_index.neighbor | 72 |
| abstract_inverted_index.positive | 56 |
| abstract_inverted_index.problems | 3 |
| abstract_inverted_index.software | 19 |
| abstract_inverted_index.K-nearest | 71 |
| abstract_inverted_index.Three-way | 35 |
| abstract_inverted_index.algorithm | 26 |
| abstract_inverted_index.decisions | 36 |
| abstract_inverted_index.detection | 8, 13, 25, 82, 85 |
| abstract_inverted_index.intrusion | 12, 24, 81 |
| abstract_inverted_index.proposed. | 38 |
| abstract_inverted_index.Simulation | 74 |
| abstract_inverted_index.algorithm. | 73 |
| abstract_inverted_index.classifying | 52 |
| abstract_inverted_index.reclassified | 68 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5036135848 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I212220629 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7200000286102295 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.84748002 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |