Hybrid Sine-Cosine Chimp optimization based feature selection with deep learning model for threat detection in IoT sensor networks Article Swipe
Mimouna Abdullah Alkhonaini
,
Alanoud Al Mazroa
,
Mohammed Aljebreen
,
Siwar Ben Haj Hassine
,
Randa Allafi
,
Ashit Kumar Dutta
,
Shtwai Alsubai
,
Aditya Khamparia
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aej.2024.05.051
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aej.2024.05.051
Internet of Things (IoT) sensor networks are connected systems of physical devices set with actuators, sensors, and communication abilities, allowing them to gather, spread, and exchange information with centralized methods. These networks are essential in numerous businesses, such as healthcare, manufacturing, agriculture, and smart cities, as they deliver real-time observation, data-driven insights, and automation. Threat recognition in IoT sensor networks is a vital feature of safeguarding the protection and consistency of interconnected systems in the IoT. As IoT sensor networks endure to increase across various industries, the vulnerability to malicious actions and cyber-attacks increases. Threat recognition utilizing deep learning (DL) leverages neural networks to examine complex patterns and anomalies in data, permitting the identification of potential safety threats. DL techniques like convolutional neural networks (CNNs) or recurrent neural networks (RNNs) excel at learning complex representations of data and feature extraction, making them suitable for identifying sophisticated attacks in different fields, including cybersecurity. This research develops a Hybrid Sine-Cosine Chimp Optimization Feature Selection with a Deep Learning (HSCCOFS-DL) approach for Threat Recognition in IoT Sensor Networks. The foremost aim of the HSCCOFS-DL system lies in the automated detection of threats using DL models. To accomplish this, the HSCCOFS-DL approach undergoes a data normalization process. Besides, the selection of features can be performed using the HSCCO algorithm. Meanwhile, the symmetrical autoencoder (SAE) technique effectively classifies threats. Finally, the sparrow search algorithm (SSA) can be applied to the selection of the hyperparameter of the SAE system. The experimental assessment of the HSCCOFS-DL technique takes place on a benchmark dataset. The simulation results indicated that the HSCCOFS-DL approach attains enhanced performance over other methods.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.aej.2024.05.051
- OA Status
- gold
- Cited By
- 13
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399428069
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4399428069Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.aej.2024.05.051Digital Object Identifier
- Title
-
Hybrid Sine-Cosine Chimp optimization based feature selection with deep learning model for threat detection in IoT sensor networksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-06-07Full publication date if available
- Authors
-
Mimouna Abdullah Alkhonaini, Alanoud Al Mazroa, Mohammed Aljebreen, Siwar Ben Haj Hassine, Randa Allafi, Ashit Kumar Dutta, Shtwai Alsubai, Aditya KhampariaList of authors in order
- Landing page
-
https://doi.org/10.1016/j.aej.2024.05.051Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.aej.2024.05.051Direct OA link when available
- Concepts
-
Sine, Computer science, Internet of Things, Wireless sensor network, Artificial intelligence, Selection (genetic algorithm), Deep learning, Feature (linguistics), Real-time computing, Embedded system, Mathematics, Computer network, Linguistics, Geometry, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 7, 2024: 6Per-year citation counts (last 5 years)
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4399428069 |
|---|---|
| doi | https://doi.org/10.1016/j.aej.2024.05.051 |
| ids.doi | https://doi.org/10.1016/j.aej.2024.05.051 |
| ids.openalex | https://openalex.org/W4399428069 |
| fwci | 10.87925931 |
| type | article |
| title | Hybrid Sine-Cosine Chimp optimization based feature selection with deep learning model for threat detection in IoT sensor networks |
| awards[0].id | https://openalex.org/G219817464 |
| awards[0].funder_id | https://openalex.org/F4320311227 |
| awards[0].display_name | |
| awards[0].funder_award_id | PSAU/2024/R/1445 |
| awards[0].funder_display_name | Prince Sattam bin Abdulaziz University |
| biblio.issue | |
| biblio.volume | 102 |
| biblio.last_page | 178 |
| biblio.first_page | 169 |
| 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/T11512 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9980999827384949 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Anomaly Detection Techniques and Applications |
| topics[2].id | https://openalex.org/T12597 |
| topics[2].field.id | https://openalex.org/fields/22 |
| topics[2].field.display_name | Engineering |
| topics[2].score | 0.9979000091552734 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2213 |
| topics[2].subfield.display_name | Safety, Risk, Reliability and Quality |
| topics[2].display_name | Fire Detection and Safety Systems |
| funders[0].id | https://openalex.org/F4320311227 |
| funders[0].ror | https://ror.org/04jt46d36 |
| funders[0].display_name | Prince Sattam bin Abdulaziz University |
| funders[1].id | https://openalex.org/F4320318809 |
| funders[1].ror | |
| funders[1].display_name | AlMaarefa University |
| funders[2].id | https://openalex.org/F4320321145 |
| funders[2].ror | https://ror.org/02f81g417 |
| funders[2].display_name | King Saud University |
| funders[3].id | https://openalex.org/F4320322484 |
| funders[3].ror | https://ror.org/05b0cyh02 |
| funders[3].display_name | Princess Nourah Bint Abdulrahman University |
| funders[4].id | https://openalex.org/F4320328654 |
| funders[4].ror | |
| funders[4].display_name | Northern Border University |
| is_xpac | False |
| apc_list.value | 860 |
| apc_list.currency | USD |
| apc_list.value_usd | 860 |
| apc_paid.value | 860 |
| apc_paid.currency | USD |
| apc_paid.value_usd | 860 |
| concepts[0].id | https://openalex.org/C186661526 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6972231268882751 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q13647261 |
| concepts[0].display_name | Sine |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6385601758956909 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C81860439 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5713032484054565 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q251212 |
| concepts[2].display_name | Internet of Things |
| concepts[3].id | https://openalex.org/C24590314 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5550119876861572 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q336038 |
| concepts[3].display_name | Wireless sensor network |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5185751914978027 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C81917197 |
| concepts[5].level | 2 |
| concepts[5].score | 0.49819231033325195 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q628760 |
| concepts[5].display_name | Selection (genetic algorithm) |
| concepts[6].id | https://openalex.org/C108583219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.47555607557296753 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[6].display_name | Deep learning |
| concepts[7].id | https://openalex.org/C2776401178 |
| concepts[7].level | 2 |
| concepts[7].score | 0.45646631717681885 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q12050496 |
| concepts[7].display_name | Feature (linguistics) |
| concepts[8].id | https://openalex.org/C79403827 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3279629349708557 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[8].display_name | Real-time computing |
| concepts[9].id | https://openalex.org/C149635348 |
| concepts[9].level | 1 |
| concepts[9].score | 0.2740042805671692 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q193040 |
| concepts[9].display_name | Embedded system |
| concepts[10].id | https://openalex.org/C33923547 |
| concepts[10].level | 0 |
| concepts[10].score | 0.12404876947402954 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[10].display_name | Mathematics |
| concepts[11].id | https://openalex.org/C31258907 |
| concepts[11].level | 1 |
| concepts[11].score | 0.11013859510421753 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[11].display_name | Computer network |
| concepts[12].id | https://openalex.org/C41895202 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[12].display_name | Linguistics |
| concepts[13].id | https://openalex.org/C2524010 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[13].display_name | Geometry |
| concepts[14].id | https://openalex.org/C138885662 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[14].display_name | Philosophy |
| keywords[0].id | https://openalex.org/keywords/sine |
| keywords[0].score | 0.6972231268882751 |
| keywords[0].display_name | Sine |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6385601758956909 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/internet-of-things |
| keywords[2].score | 0.5713032484054565 |
| keywords[2].display_name | Internet of Things |
| keywords[3].id | https://openalex.org/keywords/wireless-sensor-network |
| keywords[3].score | 0.5550119876861572 |
| keywords[3].display_name | Wireless sensor network |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5185751914978027 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/selection |
| keywords[5].score | 0.49819231033325195 |
| keywords[5].display_name | Selection (genetic algorithm) |
| keywords[6].id | https://openalex.org/keywords/deep-learning |
| keywords[6].score | 0.47555607557296753 |
| keywords[6].display_name | Deep learning |
| keywords[7].id | https://openalex.org/keywords/feature |
| keywords[7].score | 0.45646631717681885 |
| keywords[7].display_name | Feature (linguistics) |
| keywords[8].id | https://openalex.org/keywords/real-time-computing |
| keywords[8].score | 0.3279629349708557 |
| keywords[8].display_name | Real-time computing |
| keywords[9].id | https://openalex.org/keywords/embedded-system |
| keywords[9].score | 0.2740042805671692 |
| keywords[9].display_name | Embedded system |
| keywords[10].id | https://openalex.org/keywords/mathematics |
| keywords[10].score | 0.12404876947402954 |
| keywords[10].display_name | Mathematics |
| keywords[11].id | https://openalex.org/keywords/computer-network |
| keywords[11].score | 0.11013859510421753 |
| keywords[11].display_name | Computer network |
| language | en |
| locations[0].id | doi:10.1016/j.aej.2024.05.051 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764413287 |
| locations[0].source.issn | 1110-0168, 2090-2670 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1110-0168 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Alexandria Engineering Journal |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| 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 | Alexandria Engineering Journal |
| locations[0].landing_page_url | https://doi.org/10.1016/j.aej.2024.05.051 |
| locations[1].id | pmh:oai:doaj.org/article:766fe94a383e4f01b2fb525774622304 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Alexandria Engineering Journal, Vol 102, Iss , Pp 169-178 (2024) |
| locations[1].landing_page_url | https://doaj.org/article/766fe94a383e4f01b2fb525774622304 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5011560990 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-1120-5891 |
| authorships[0].author.display_name | Mimouna Abdullah Alkhonaini |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Mimouna Abdullah Alkhonaini |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5093345795 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6201-0410 |
| authorships[1].author.display_name | Alanoud Al Mazroa |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Alanoud Al Mazroa |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5025820678 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6295-733X |
| authorships[2].author.display_name | Mohammed Aljebreen |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Mohammed Aljebreen |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5089886514 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-9501-1930 |
| authorships[3].author.display_name | Siwar Ben Haj Hassine |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Siwar Ben Haj Hassine |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5021273283 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-9858-5160 |
| authorships[4].author.display_name | Randa Allafi |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Randa Allafi |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5073416127 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-1208-2678 |
| authorships[5].author.display_name | Ashit Kumar Dutta |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Ashit Kumar Dutta |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5000028489 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-6584-7400 |
| authorships[6].author.display_name | Shtwai Alsubai |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Shtwai Alsubai |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5005282924 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-9019-8230 |
| authorships[7].author.display_name | Aditya Khamparia |
| authorships[7].author_position | last |
| authorships[7].raw_author_name | Aditya Khamparia |
| authorships[7].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.aej.2024.05.051 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Hybrid Sine-Cosine Chimp optimization based feature selection with deep learning model for threat detection in IoT sensor networks |
| 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/W4245926026, https://openalex.org/W4311097251, https://openalex.org/W2586548817, https://openalex.org/W2625093826, https://openalex.org/W2950174689, https://openalex.org/W4200598720, https://openalex.org/W2921026492, https://openalex.org/W4247463117, https://openalex.org/W4361251261, https://openalex.org/W3031181660 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 7 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 6 |
| locations_count | 2 |
| best_oa_location.id | doi:10.1016/j.aej.2024.05.051 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764413287 |
| best_oa_location.source.issn | 1110-0168, 2090-2670 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1110-0168 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Alexandria Engineering Journal |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| 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 | Alexandria Engineering Journal |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.aej.2024.05.051 |
| primary_location.id | doi:10.1016/j.aej.2024.05.051 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764413287 |
| primary_location.source.issn | 1110-0168, 2090-2670 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1110-0168 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Alexandria Engineering Journal |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| 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 | Alexandria Engineering Journal |
| primary_location.landing_page_url | https://doi.org/10.1016/j.aej.2024.05.051 |
| publication_date | 2024-06-07 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W6852913739, https://openalex.org/W4296010534, https://openalex.org/W4305057905, https://openalex.org/W4226367037, https://openalex.org/W6786793919, https://openalex.org/W4297094626, https://openalex.org/W4377138364, https://openalex.org/W6770607121, https://openalex.org/W6845864873, https://openalex.org/W4206017326, https://openalex.org/W4386097412, https://openalex.org/W6800597013, https://openalex.org/W4290694824, https://openalex.org/W6849811855, https://openalex.org/W4281817087, https://openalex.org/W6857831521, https://openalex.org/W4313244277, https://openalex.org/W4391345122, https://openalex.org/W4388933095, https://openalex.org/W6860811413, https://openalex.org/W6860537684, https://openalex.org/W2525336835, https://openalex.org/W4388820062, https://openalex.org/W3111779061, https://openalex.org/W2990606942, https://openalex.org/W4390837630, https://openalex.org/W3196438265, https://openalex.org/W4387353351, https://openalex.org/W4376605853, https://openalex.org/W4321325892, https://openalex.org/W4306631453, https://openalex.org/W4390932995 |
| referenced_works_count | 32 |
| abstract_inverted_index.a | 61, 155, 163, 199, 253 |
| abstract_inverted_index.As | 76 |
| abstract_inverted_index.DL | 118, 190 |
| abstract_inverted_index.To | 192 |
| abstract_inverted_index.as | 38, 45 |
| abstract_inverted_index.at | 131 |
| abstract_inverted_index.be | 209, 231 |
| abstract_inverted_index.in | 34, 56, 73, 109, 147, 171, 183 |
| abstract_inverted_index.is | 60 |
| abstract_inverted_index.of | 1, 9, 64, 70, 114, 135, 178, 187, 206, 236, 239, 246 |
| abstract_inverted_index.on | 252 |
| abstract_inverted_index.or | 125 |
| abstract_inverted_index.to | 21, 81, 88, 103, 233 |
| abstract_inverted_index.IoT | 57, 77, 172 |
| abstract_inverted_index.SAE | 241 |
| abstract_inverted_index.The | 175, 243, 256 |
| abstract_inverted_index.aim | 177 |
| abstract_inverted_index.and | 16, 24, 42, 52, 68, 91, 107, 137 |
| abstract_inverted_index.are | 6, 32 |
| abstract_inverted_index.can | 208, 230 |
| abstract_inverted_index.for | 143, 168 |
| abstract_inverted_index.set | 12 |
| abstract_inverted_index.the | 66, 74, 86, 112, 179, 184, 195, 204, 212, 216, 225, 234, 237, 240, 247, 261 |
| abstract_inverted_index.(DL) | 99 |
| abstract_inverted_index.Deep | 164 |
| abstract_inverted_index.IoT. | 75 |
| abstract_inverted_index.This | 152 |
| abstract_inverted_index.data | 136, 200 |
| abstract_inverted_index.deep | 97 |
| abstract_inverted_index.lies | 182 |
| abstract_inverted_index.like | 120 |
| abstract_inverted_index.over | 267 |
| abstract_inverted_index.such | 37 |
| abstract_inverted_index.that | 260 |
| abstract_inverted_index.them | 20, 141 |
| abstract_inverted_index.they | 46 |
| abstract_inverted_index.with | 13, 27, 162 |
| abstract_inverted_index.(IoT) | 3 |
| abstract_inverted_index.(SAE) | 219 |
| abstract_inverted_index.(SSA) | 229 |
| abstract_inverted_index.Chimp | 158 |
| abstract_inverted_index.HSCCO | 213 |
| abstract_inverted_index.These | 30 |
| abstract_inverted_index.data, | 110 |
| abstract_inverted_index.excel | 130 |
| abstract_inverted_index.other | 268 |
| abstract_inverted_index.place | 251 |
| abstract_inverted_index.smart | 43 |
| abstract_inverted_index.takes | 250 |
| abstract_inverted_index.this, | 194 |
| abstract_inverted_index.using | 189, 211 |
| abstract_inverted_index.vital | 62 |
| abstract_inverted_index.(CNNs) | 124 |
| abstract_inverted_index.(RNNs) | 129 |
| abstract_inverted_index.Hybrid | 156 |
| abstract_inverted_index.Sensor | 173 |
| abstract_inverted_index.Things | 2 |
| abstract_inverted_index.Threat | 54, 94, 169 |
| abstract_inverted_index.across | 83 |
| abstract_inverted_index.endure | 80 |
| abstract_inverted_index.making | 140 |
| abstract_inverted_index.neural | 101, 122, 127 |
| abstract_inverted_index.safety | 116 |
| abstract_inverted_index.search | 227 |
| abstract_inverted_index.sensor | 4, 58, 78 |
| abstract_inverted_index.system | 181 |
| abstract_inverted_index.Feature | 160 |
| abstract_inverted_index.actions | 90 |
| abstract_inverted_index.applied | 232 |
| abstract_inverted_index.attacks | 146 |
| abstract_inverted_index.attains | 264 |
| abstract_inverted_index.cities, | 44 |
| abstract_inverted_index.complex | 105, 133 |
| abstract_inverted_index.deliver | 47 |
| abstract_inverted_index.devices | 11 |
| abstract_inverted_index.examine | 104 |
| abstract_inverted_index.feature | 63, 138 |
| abstract_inverted_index.fields, | 149 |
| abstract_inverted_index.gather, | 22 |
| abstract_inverted_index.models. | 191 |
| abstract_inverted_index.results | 258 |
| abstract_inverted_index.sparrow | 226 |
| abstract_inverted_index.spread, | 23 |
| abstract_inverted_index.system. | 242 |
| abstract_inverted_index.systems | 8, 72 |
| abstract_inverted_index.threats | 188 |
| abstract_inverted_index.various | 84 |
| abstract_inverted_index.Besides, | 203 |
| abstract_inverted_index.Finally, | 224 |
| abstract_inverted_index.Internet | 0 |
| abstract_inverted_index.Learning | 165 |
| abstract_inverted_index.allowing | 19 |
| abstract_inverted_index.approach | 167, 197, 263 |
| abstract_inverted_index.dataset. | 255 |
| abstract_inverted_index.develops | 154 |
| abstract_inverted_index.enhanced | 265 |
| abstract_inverted_index.exchange | 25 |
| abstract_inverted_index.features | 207 |
| abstract_inverted_index.foremost | 176 |
| abstract_inverted_index.increase | 82 |
| abstract_inverted_index.learning | 98, 132 |
| abstract_inverted_index.methods. | 29, 269 |
| abstract_inverted_index.networks | 5, 31, 59, 79, 102, 123, 128 |
| abstract_inverted_index.numerous | 35 |
| abstract_inverted_index.patterns | 106 |
| abstract_inverted_index.physical | 10 |
| abstract_inverted_index.process. | 202 |
| abstract_inverted_index.research | 153 |
| abstract_inverted_index.sensors, | 15 |
| abstract_inverted_index.suitable | 142 |
| abstract_inverted_index.threats. | 117, 223 |
| abstract_inverted_index.Networks. | 174 |
| abstract_inverted_index.Selection | 161 |
| abstract_inverted_index.algorithm | 228 |
| abstract_inverted_index.anomalies | 108 |
| abstract_inverted_index.automated | 185 |
| abstract_inverted_index.benchmark | 254 |
| abstract_inverted_index.connected | 7 |
| abstract_inverted_index.detection | 186 |
| abstract_inverted_index.different | 148 |
| abstract_inverted_index.essential | 33 |
| abstract_inverted_index.including | 150 |
| abstract_inverted_index.indicated | 259 |
| abstract_inverted_index.insights, | 51 |
| abstract_inverted_index.leverages | 100 |
| abstract_inverted_index.malicious | 89 |
| abstract_inverted_index.performed | 210 |
| abstract_inverted_index.potential | 115 |
| abstract_inverted_index.real-time | 48 |
| abstract_inverted_index.recurrent | 126 |
| abstract_inverted_index.selection | 205, 235 |
| abstract_inverted_index.technique | 220, 249 |
| abstract_inverted_index.undergoes | 198 |
| abstract_inverted_index.utilizing | 96 |
| abstract_inverted_index.HSCCOFS-DL | 180, 196, 248, 262 |
| abstract_inverted_index.Meanwhile, | 215 |
| abstract_inverted_index.abilities, | 18 |
| abstract_inverted_index.accomplish | 193 |
| abstract_inverted_index.actuators, | 14 |
| abstract_inverted_index.algorithm. | 214 |
| abstract_inverted_index.assessment | 245 |
| abstract_inverted_index.classifies | 222 |
| abstract_inverted_index.increases. | 93 |
| abstract_inverted_index.permitting | 111 |
| abstract_inverted_index.protection | 67 |
| abstract_inverted_index.simulation | 257 |
| abstract_inverted_index.techniques | 119 |
| abstract_inverted_index.Recognition | 170 |
| abstract_inverted_index.Sine-Cosine | 157 |
| abstract_inverted_index.autoencoder | 218 |
| abstract_inverted_index.automation. | 53 |
| abstract_inverted_index.businesses, | 36 |
| abstract_inverted_index.centralized | 28 |
| abstract_inverted_index.consistency | 69 |
| abstract_inverted_index.data-driven | 50 |
| abstract_inverted_index.effectively | 221 |
| abstract_inverted_index.extraction, | 139 |
| abstract_inverted_index.healthcare, | 39 |
| abstract_inverted_index.identifying | 144 |
| abstract_inverted_index.industries, | 85 |
| abstract_inverted_index.information | 26 |
| abstract_inverted_index.performance | 266 |
| abstract_inverted_index.recognition | 55, 95 |
| abstract_inverted_index.symmetrical | 217 |
| abstract_inverted_index.(HSCCOFS-DL) | 166 |
| abstract_inverted_index.Optimization | 159 |
| abstract_inverted_index.agriculture, | 41 |
| abstract_inverted_index.experimental | 244 |
| abstract_inverted_index.observation, | 49 |
| abstract_inverted_index.safeguarding | 65 |
| abstract_inverted_index.communication | 17 |
| abstract_inverted_index.convolutional | 121 |
| abstract_inverted_index.cyber-attacks | 92 |
| abstract_inverted_index.normalization | 201 |
| abstract_inverted_index.sophisticated | 145 |
| abstract_inverted_index.vulnerability | 87 |
| abstract_inverted_index.cybersecurity. | 151 |
| abstract_inverted_index.hyperparameter | 238 |
| abstract_inverted_index.identification | 113 |
| abstract_inverted_index.interconnected | 71 |
| abstract_inverted_index.manufacturing, | 40 |
| abstract_inverted_index.representations | 134 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.96823087 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |