ENHANCING CYBERSECURITY VIA ANOMALY RECOGNITION USING THERMAL EXCHANGE FRACTALS OPTIMIZATION WITH DEEP LEARNING ON IOT NETWORKS Article Swipe
Asma A. Alhashmi
,
Hayam Alamro
,
Mohammed Aljebreen
,
Mohammed Alghamdi
,
Abeer A. K. Alharbi
,
Ahmed Mahmud
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1142/s0218348x25400353
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1142/s0218348x25400353
The Internet of Things (IoT) refers to the interconnected network of objects and devices that seamlessly communicate and share information. The need for robust cybersecurity measures becomes paramount with the increase of IoT devices, ranging from smart home devices to industrial sensors. The inherent vulnerability of the IoT ecosystem to cyber threats necessitates cutting-edge security protocols to ensure the integrity of connected systems and safeguard sensitive information. IoT security is crucial to protect against potential manipulation of connected devices, unauthorized access, and data breaches. An essential facet of IoT cybersecurity, Anomaly detection, includes the detection of unusual behaviors or patterns in device activity or network traffic in many complex systems that may indicate security breaches. Deep learning (DL), with its ability to analyze complex and vast datasets, has improved anomaly detection in IoT environments. By leveraging DL techniques, IoT systems can better adapt to evolving cyber threats, which offer a proactive defense system against complex cyber threats in various complex systems. In essence, incorporating anomaly detection and DL within the IoT cybersecurity framework is crucial to ensure the entire IoT ecosystem’s trustworthiness and fortify interconnected devices’ resilience. This study presents an anomaly recognition using fractals thermal exchange optimization with deep learning (ARA-TEODL) technique for cybersecurity on IoT Networks. The ARA-TEODL technique focuses on identifying anomalous behavior in the IoT network to achieve cybersecurity. In the ARA-TEODL technique, Z-score normalization is primarily used to scale the input networking data. Besides, the selection of features takes place utilizing the chimp fractals optimization algorithm (ChOA). Moreover, a modified Mogrifier long short-term memory (MM-LSTM) model is used to identify anomalies in the network. Finally, the hyperparameter tuning process takes place using the TEO algorithm. The experimental evaluation of the ARA-TEODL technique takes place using a benchmark dataset. The experimental results stated that the ARA-TEODL technique reaches optimal cybersecurity in the IoT networks.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1142/s0218348x25400353
- OA Status
- hybrid
- References
- 16
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405866909
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405866909Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1142/s0218348x25400353Digital Object Identifier
- Title
-
ENHANCING CYBERSECURITY VIA ANOMALY RECOGNITION USING THERMAL EXCHANGE FRACTALS OPTIMIZATION WITH DEEP LEARNING ON IOT NETWORKSWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-29Full publication date if available
- Authors
-
Asma A. Alhashmi, Hayam Alamro, Mohammed Aljebreen, Mohammed Alghamdi, Abeer A. K. Alharbi, Ahmed MahmudList of authors in order
- Landing page
-
https://doi.org/10.1142/s0218348x25400353Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1142/s0218348x25400353Direct OA link when available
- Concepts
-
Fractal, Anomaly detection, Internet of Things, Anomaly (physics), Computer science, Artificial intelligence, Deep learning, Computer security, Pattern recognition (psychology), Mathematics, Condensed matter physics, Physics, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
16Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405866909 |
|---|---|
| doi | https://doi.org/10.1142/s0218348x25400353 |
| ids.doi | https://doi.org/10.1142/s0218348x25400353 |
| ids.openalex | https://openalex.org/W4405866909 |
| fwci | 0.0 |
| type | article |
| title | ENHANCING CYBERSECURITY VIA ANOMALY RECOGNITION USING THERMAL EXCHANGE FRACTALS OPTIMIZATION WITH DEEP LEARNING ON IOT NETWORKS |
| biblio.issue | 02 |
| biblio.volume | 33 |
| biblio.last_page | |
| biblio.first_page | |
| 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 | 0.9333999752998352 |
| 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 |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C40636538 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6440799236297607 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q81392 |
| concepts[0].display_name | Fractal |
| concepts[1].id | https://openalex.org/C739882 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6294329762458801 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q3560506 |
| concepts[1].display_name | Anomaly detection |
| concepts[2].id | https://openalex.org/C81860439 |
| concepts[2].level | 2 |
| concepts[2].score | 0.589535653591156 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q251212 |
| concepts[2].display_name | Internet of Things |
| concepts[3].id | https://openalex.org/C12997251 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5666298270225525 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q567560 |
| concepts[3].display_name | Anomaly (physics) |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.5399792790412903 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C154945302 |
| concepts[5].level | 1 |
| concepts[5].score | 0.5307868719100952 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[5].display_name | Artificial intelligence |
| concepts[6].id | https://openalex.org/C108583219 |
| concepts[6].level | 2 |
| concepts[6].score | 0.46604979038238525 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[6].display_name | Deep learning |
| concepts[7].id | https://openalex.org/C38652104 |
| concepts[7].level | 1 |
| concepts[7].score | 0.40740248560905457 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[7].display_name | Computer security |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.35823991894721985 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C33923547 |
| concepts[9].level | 0 |
| concepts[9].score | 0.2028723657131195 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[9].display_name | Mathematics |
| concepts[10].id | https://openalex.org/C26873012 |
| concepts[10].level | 1 |
| concepts[10].score | 0.13964617252349854 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q214781 |
| concepts[10].display_name | Condensed matter physics |
| concepts[11].id | https://openalex.org/C121332964 |
| concepts[11].level | 0 |
| concepts[11].score | 0.11835426092147827 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[11].display_name | Physics |
| concepts[12].id | https://openalex.org/C134306372 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[12].display_name | Mathematical analysis |
| keywords[0].id | https://openalex.org/keywords/fractal |
| keywords[0].score | 0.6440799236297607 |
| keywords[0].display_name | Fractal |
| keywords[1].id | https://openalex.org/keywords/anomaly-detection |
| keywords[1].score | 0.6294329762458801 |
| keywords[1].display_name | Anomaly detection |
| keywords[2].id | https://openalex.org/keywords/internet-of-things |
| keywords[2].score | 0.589535653591156 |
| keywords[2].display_name | Internet of Things |
| keywords[3].id | https://openalex.org/keywords/anomaly |
| keywords[3].score | 0.5666298270225525 |
| keywords[3].display_name | Anomaly (physics) |
| keywords[4].id | https://openalex.org/keywords/computer-science |
| keywords[4].score | 0.5399792790412903 |
| keywords[4].display_name | Computer science |
| keywords[5].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[5].score | 0.5307868719100952 |
| keywords[5].display_name | Artificial intelligence |
| keywords[6].id | https://openalex.org/keywords/deep-learning |
| keywords[6].score | 0.46604979038238525 |
| keywords[6].display_name | Deep learning |
| keywords[7].id | https://openalex.org/keywords/computer-security |
| keywords[7].score | 0.40740248560905457 |
| keywords[7].display_name | Computer security |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.35823991894721985 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/mathematics |
| keywords[9].score | 0.2028723657131195 |
| keywords[9].display_name | Mathematics |
| keywords[10].id | https://openalex.org/keywords/condensed-matter-physics |
| keywords[10].score | 0.13964617252349854 |
| keywords[10].display_name | Condensed matter physics |
| keywords[11].id | https://openalex.org/keywords/physics |
| keywords[11].score | 0.11835426092147827 |
| keywords[11].display_name | Physics |
| language | en |
| locations[0].id | doi:10.1142/s0218348x25400353 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S204703277 |
| locations[0].source.issn | 0218-348X, 1793-6543 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 0218-348X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Fractals |
| locations[0].source.host_organization | https://openalex.org/P4310319815 |
| locations[0].source.host_organization_name | World Scientific |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310319815 |
| locations[0].source.host_organization_lineage_names | World Scientific |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Fractals |
| locations[0].landing_page_url | https://doi.org/10.1142/s0218348x25400353 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5012876317 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-7871-7069 |
| authorships[0].author.display_name | Asma A. Alhashmi |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Asma A. Alhashmi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5030440060 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3157-8086 |
| authorships[1].author.display_name | Hayam Alamro |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hayam Alamro |
| 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/A5057472157 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-5042-0690 |
| authorships[3].author.display_name | Mohammed Alghamdi |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Mohammed Alghamdi |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5093603822 |
| authorships[4].author.orcid | https://orcid.org/0009-0003-5075-7933 |
| authorships[4].author.display_name | Abeer A. K. Alharbi |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Abeer A. K. Alharbi |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5113763366 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-3049-7471 |
| authorships[5].author.display_name | Ahmed Mahmud |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Ahmed Mahmud |
| authorships[5].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/10.1142/s0218348x25400353 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | ENHANCING CYBERSECURITY VIA ANOMALY RECOGNITION USING THERMAL EXCHANGE FRACTALS OPTIMIZATION WITH DEEP LEARNING ON IOT 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 | 0.9333999752998352 |
| 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/W2806741695, https://openalex.org/W4290647774, https://openalex.org/W3189286258, https://openalex.org/W3207797160, https://openalex.org/W3210364259, https://openalex.org/W4300558037, https://openalex.org/W2667207928, https://openalex.org/W2912112202, https://openalex.org/W4377864969, https://openalex.org/W3120251014 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1142/s0218348x25400353 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S204703277 |
| best_oa_location.source.issn | 0218-348X, 1793-6543 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 0218-348X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Fractals |
| best_oa_location.source.host_organization | https://openalex.org/P4310319815 |
| best_oa_location.source.host_organization_name | World Scientific |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310319815 |
| best_oa_location.source.host_organization_lineage_names | World Scientific |
| best_oa_location.license | cc-by-nc-nd |
| 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-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Fractals |
| best_oa_location.landing_page_url | https://doi.org/10.1142/s0218348x25400353 |
| primary_location.id | doi:10.1142/s0218348x25400353 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S204703277 |
| primary_location.source.issn | 0218-348X, 1793-6543 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 0218-348X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Fractals |
| primary_location.source.host_organization | https://openalex.org/P4310319815 |
| primary_location.source.host_organization_name | World Scientific |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310319815 |
| primary_location.source.host_organization_lineage_names | World Scientific |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Fractals |
| primary_location.landing_page_url | https://doi.org/10.1142/s0218348x25400353 |
| publication_date | 2024-12-29 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W4391348949, https://openalex.org/W4212986808, https://openalex.org/W4390659333, https://openalex.org/W3215059392, https://openalex.org/W4322620863, https://openalex.org/W4213455216, https://openalex.org/W4386219824, https://openalex.org/W4391052592, https://openalex.org/W4388948083, https://openalex.org/W4307190418, https://openalex.org/W4353110502, https://openalex.org/W4390947966, https://openalex.org/W4289130121, https://openalex.org/W4389089808, https://openalex.org/W4391326055, https://openalex.org/W4388820062 |
| referenced_works_count | 16 |
| abstract_inverted_index.a | 149, 253, 290 |
| abstract_inverted_index.An | 84 |
| abstract_inverted_index.By | 134 |
| abstract_inverted_index.DL | 136, 167 |
| abstract_inverted_index.In | 161, 223 |
| abstract_inverted_index.an | 190 |
| abstract_inverted_index.in | 100, 106, 131, 157, 216, 266, 304 |
| abstract_inverted_index.is | 69, 173, 229, 261 |
| abstract_inverted_index.of | 2, 10, 31, 45, 60, 76, 87, 95, 241, 283 |
| abstract_inverted_index.on | 205, 212 |
| abstract_inverted_index.or | 98, 103 |
| abstract_inverted_index.to | 6, 39, 49, 56, 71, 121, 143, 175, 220, 232, 263 |
| abstract_inverted_index.IoT | 32, 47, 67, 88, 132, 138, 170, 179, 206, 218, 306 |
| abstract_inverted_index.TEO | 278 |
| abstract_inverted_index.The | 0, 20, 42, 208, 280, 293 |
| abstract_inverted_index.and | 12, 17, 63, 81, 124, 166, 182 |
| abstract_inverted_index.can | 140 |
| abstract_inverted_index.for | 22, 203 |
| abstract_inverted_index.has | 127 |
| abstract_inverted_index.its | 119 |
| abstract_inverted_index.may | 111 |
| abstract_inverted_index.the | 7, 29, 46, 58, 93, 169, 177, 217, 224, 234, 239, 246, 267, 270, 277, 284, 298, 305 |
| abstract_inverted_index.Deep | 115 |
| abstract_inverted_index.This | 187 |
| abstract_inverted_index.data | 82 |
| abstract_inverted_index.deep | 199 |
| abstract_inverted_index.from | 35 |
| abstract_inverted_index.home | 37 |
| abstract_inverted_index.long | 256 |
| abstract_inverted_index.many | 107 |
| abstract_inverted_index.need | 21 |
| abstract_inverted_index.that | 14, 110, 297 |
| abstract_inverted_index.used | 231, 262 |
| abstract_inverted_index.vast | 125 |
| abstract_inverted_index.with | 28, 118, 198 |
| abstract_inverted_index.(DL), | 117 |
| abstract_inverted_index.(IoT) | 4 |
| abstract_inverted_index.adapt | 142 |
| abstract_inverted_index.chimp | 247 |
| abstract_inverted_index.cyber | 50, 145, 155 |
| abstract_inverted_index.data. | 237 |
| abstract_inverted_index.facet | 86 |
| abstract_inverted_index.input | 235 |
| abstract_inverted_index.model | 260 |
| abstract_inverted_index.offer | 148 |
| abstract_inverted_index.place | 244, 275, 288 |
| abstract_inverted_index.scale | 233 |
| abstract_inverted_index.share | 18 |
| abstract_inverted_index.smart | 36 |
| abstract_inverted_index.study | 188 |
| abstract_inverted_index.takes | 243, 274, 287 |
| abstract_inverted_index.using | 193, 276, 289 |
| abstract_inverted_index.which | 147 |
| abstract_inverted_index.Things | 3 |
| abstract_inverted_index.better | 141 |
| abstract_inverted_index.device | 101 |
| abstract_inverted_index.ensure | 57, 176 |
| abstract_inverted_index.entire | 178 |
| abstract_inverted_index.memory | 258 |
| abstract_inverted_index.refers | 5 |
| abstract_inverted_index.robust | 23 |
| abstract_inverted_index.stated | 296 |
| abstract_inverted_index.system | 152 |
| abstract_inverted_index.tuning | 272 |
| abstract_inverted_index.within | 168 |
| abstract_inverted_index.(ChOA). | 251 |
| abstract_inverted_index.Anomaly | 90 |
| abstract_inverted_index.Z-score | 227 |
| abstract_inverted_index.ability | 120 |
| abstract_inverted_index.access, | 80 |
| abstract_inverted_index.achieve | 221 |
| abstract_inverted_index.against | 73, 153 |
| abstract_inverted_index.analyze | 122 |
| abstract_inverted_index.anomaly | 129, 164, 191 |
| abstract_inverted_index.becomes | 26 |
| abstract_inverted_index.complex | 108, 123, 154, 159 |
| abstract_inverted_index.crucial | 70, 174 |
| abstract_inverted_index.defense | 151 |
| abstract_inverted_index.devices | 13, 38 |
| abstract_inverted_index.focuses | 211 |
| abstract_inverted_index.fortify | 183 |
| abstract_inverted_index.network | 9, 104, 219 |
| abstract_inverted_index.objects | 11 |
| abstract_inverted_index.optimal | 302 |
| abstract_inverted_index.process | 273 |
| abstract_inverted_index.protect | 72 |
| abstract_inverted_index.ranging | 34 |
| abstract_inverted_index.reaches | 301 |
| abstract_inverted_index.results | 295 |
| abstract_inverted_index.systems | 62, 109, 139 |
| abstract_inverted_index.thermal | 195 |
| abstract_inverted_index.threats | 51, 156 |
| abstract_inverted_index.traffic | 105 |
| abstract_inverted_index.unusual | 96 |
| abstract_inverted_index.various | 158 |
| abstract_inverted_index.Besides, | 238 |
| abstract_inverted_index.Finally, | 269 |
| abstract_inverted_index.Internet | 1 |
| abstract_inverted_index.activity | 102 |
| abstract_inverted_index.behavior | 215 |
| abstract_inverted_index.dataset. | 292 |
| abstract_inverted_index.devices, | 33, 78 |
| abstract_inverted_index.essence, | 162 |
| abstract_inverted_index.evolving | 144 |
| abstract_inverted_index.exchange | 196 |
| abstract_inverted_index.features | 242 |
| abstract_inverted_index.fractals | 194, 248 |
| abstract_inverted_index.identify | 264 |
| abstract_inverted_index.improved | 128 |
| abstract_inverted_index.includes | 92 |
| abstract_inverted_index.increase | 30 |
| abstract_inverted_index.indicate | 112 |
| abstract_inverted_index.inherent | 43 |
| abstract_inverted_index.learning | 116, 200 |
| abstract_inverted_index.measures | 25 |
| abstract_inverted_index.modified | 254 |
| abstract_inverted_index.network. | 268 |
| abstract_inverted_index.patterns | 99 |
| abstract_inverted_index.presents | 189 |
| abstract_inverted_index.security | 54, 68, 113 |
| abstract_inverted_index.sensors. | 41 |
| abstract_inverted_index.systems. | 160 |
| abstract_inverted_index.threats, | 146 |
| abstract_inverted_index.(MM-LSTM) | 259 |
| abstract_inverted_index.ARA-TEODL | 209, 225, 285, 299 |
| abstract_inverted_index.Mogrifier | 255 |
| abstract_inverted_index.Moreover, | 252 |
| abstract_inverted_index.Networks. | 207 |
| abstract_inverted_index.algorithm | 250 |
| abstract_inverted_index.anomalies | 265 |
| abstract_inverted_index.anomalous | 214 |
| abstract_inverted_index.behaviors | 97 |
| abstract_inverted_index.benchmark | 291 |
| abstract_inverted_index.breaches. | 83, 114 |
| abstract_inverted_index.connected | 61, 77 |
| abstract_inverted_index.datasets, | 126 |
| abstract_inverted_index.detection | 94, 130, 165 |
| abstract_inverted_index.ecosystem | 48 |
| abstract_inverted_index.essential | 85 |
| abstract_inverted_index.framework | 172 |
| abstract_inverted_index.integrity | 59 |
| abstract_inverted_index.networks. | 307 |
| abstract_inverted_index.paramount | 27 |
| abstract_inverted_index.potential | 74 |
| abstract_inverted_index.primarily | 230 |
| abstract_inverted_index.proactive | 150 |
| abstract_inverted_index.protocols | 55 |
| abstract_inverted_index.safeguard | 64 |
| abstract_inverted_index.selection | 240 |
| abstract_inverted_index.sensitive | 65 |
| abstract_inverted_index.technique | 202, 210, 286, 300 |
| abstract_inverted_index.utilizing | 245 |
| abstract_inverted_index.algorithm. | 279 |
| abstract_inverted_index.detection, | 91 |
| abstract_inverted_index.devices’ | 185 |
| abstract_inverted_index.evaluation | 282 |
| abstract_inverted_index.industrial | 40 |
| abstract_inverted_index.leveraging | 135 |
| abstract_inverted_index.networking | 236 |
| abstract_inverted_index.seamlessly | 15 |
| abstract_inverted_index.short-term | 257 |
| abstract_inverted_index.technique, | 226 |
| abstract_inverted_index.(ARA-TEODL) | 201 |
| abstract_inverted_index.communicate | 16 |
| abstract_inverted_index.identifying | 213 |
| abstract_inverted_index.recognition | 192 |
| abstract_inverted_index.resilience. | 186 |
| abstract_inverted_index.techniques, | 137 |
| abstract_inverted_index.cutting-edge | 53 |
| abstract_inverted_index.experimental | 281, 294 |
| abstract_inverted_index.information. | 19, 66 |
| abstract_inverted_index.manipulation | 75 |
| abstract_inverted_index.necessitates | 52 |
| abstract_inverted_index.optimization | 197, 249 |
| abstract_inverted_index.unauthorized | 79 |
| abstract_inverted_index.cybersecurity | 24, 171, 204, 303 |
| abstract_inverted_index.ecosystem’s | 180 |
| abstract_inverted_index.environments. | 133 |
| abstract_inverted_index.incorporating | 163 |
| abstract_inverted_index.normalization | 228 |
| abstract_inverted_index.vulnerability | 44 |
| abstract_inverted_index.cybersecurity, | 89 |
| abstract_inverted_index.cybersecurity. | 222 |
| abstract_inverted_index.hyperparameter | 271 |
| abstract_inverted_index.interconnected | 8, 184 |
| abstract_inverted_index.trustworthiness | 181 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/12 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Responsible consumption and production |
| citation_normalized_percentile.value | 0.33343668 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |