A Survey of the Recent Trends in Deep Learning Based Malware Detection Article Swipe
Umm-e-Hani Tayyab
,
Faiza Babar Khan
,
Muhammad Hanif Durad
,
Asifullah Khan
,
Yeon Soo Lee
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/jcp2040041
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/jcp2040041
Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying malicious activity. Malicious activities potentially lead to a system breach or data compromise. Various tools and anti-malware products exist for the detection of malware and cyberattacks utilizing IOCs, but all have several shortcomings. For instance, anti-malware systems make use of malware signatures, requiring a database containing such signatures to be constantly updated. Additionally, this technique does not work for zero-day attacks or variants of existing malware. In the quest to fight zero-day attacks, the research paradigm shifted from primitive methods to classical machine learning-based methods. Primitive methods are limited in catering to anti-analysis techniques against zero-day attacks. Hence, the direction of research moved towards methods utilizing classic machine learning, however, machine learning methods also come with certain limitations. They may include but not limited to the latency/lag introduced by feature-engineering phase on the entire training dataset as opposed to the real-time analysis requirement. Likewise, additional layers of data engineering to cater to the increasing volume of data introduces further delays. It led to the use of deep learning-based methods for malware detection. With the speedy occurrence of zero-day malware, researchers chose to experiment with few shot learning so that reliable solutions can be produced for malware detection with even a small amount of data at hand for training. In this paper, we surveyed several possible strategies to support the real-time detection of malware and propose a hierarchical model to discover security events or threats in real-time. A key focus in this survey is on the use of Deep Learning-based methods. Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning. We compare Deep Learning-based approaches with conventional machine learning based approaches and primitive (statistical analysis based) methods commonly reported in the literature.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jcp2040041
- https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623
- OA Status
- gold
- Cited By
- 78
- References
- 87
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4297477879
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4297477879Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jcp2040041Digital Object Identifier
- Title
-
A Survey of the Recent Trends in Deep Learning Based Malware DetectionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-09-28Full publication date if available
- Authors
-
Umm-e-Hani Tayyab, Faiza Babar Khan, Muhammad Hanif Durad, Asifullah Khan, Yeon Soo LeeList of authors in order
- Landing page
-
https://doi.org/10.3390/jcp2040041Publisher landing page
- PDF URL
-
https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623Direct OA link when available
- Concepts
-
Malware, Computer science, Machine learning, Feature engineering, Artificial intelligence, Cryptovirology, Compromise, Malware analysis, Deep learning, Computer security, Data mining, Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
78Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 28, 2023: 26, 2022: 4Per-year citation counts (last 5 years)
- References (count)
-
87Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4297477879 |
|---|---|
| doi | https://doi.org/10.3390/jcp2040041 |
| ids.doi | https://doi.org/10.3390/jcp2040041 |
| ids.openalex | https://openalex.org/W4297477879 |
| fwci | 15.20666833 |
| type | article |
| title | A Survey of the Recent Trends in Deep Learning Based Malware Detection |
| biblio.issue | 4 |
| biblio.volume | 2 |
| biblio.last_page | 829 |
| biblio.first_page | 800 |
| topics[0].id | https://openalex.org/T11241 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 1.0 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1711 |
| topics[0].subfield.display_name | Signal Processing |
| topics[0].display_name | Advanced Malware Detection Techniques |
| topics[1].id | https://openalex.org/T10400 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9995999932289124 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1705 |
| topics[1].subfield.display_name | Computer Networks and Communications |
| topics[1].display_name | Network Security and Intrusion Detection |
| topics[2].id | https://openalex.org/T11512 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9986000061035156 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Anomaly Detection Techniques and Applications |
| is_xpac | False |
| apc_list.value | 1000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 1082 |
| apc_paid.value | 1000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 1082 |
| concepts[0].id | https://openalex.org/C541664917 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9237269759178162 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q14001 |
| concepts[0].display_name | Malware |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.7998418807983398 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C119857082 |
| concepts[2].level | 1 |
| concepts[2].score | 0.6202881336212158 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[2].display_name | Machine learning |
| concepts[3].id | https://openalex.org/C2778827112 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6105398535728455 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q22245680 |
| concepts[3].display_name | Feature engineering |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.6038026809692383 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C84525096 |
| concepts[5].level | 3 |
| concepts[5].score | 0.5272069573402405 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q3506050 |
| concepts[5].display_name | Cryptovirology |
| concepts[6].id | https://openalex.org/C46355384 |
| concepts[6].level | 2 |
| concepts[6].score | 0.5070266723632812 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q726686 |
| concepts[6].display_name | Compromise |
| concepts[7].id | https://openalex.org/C2779395397 |
| concepts[7].level | 3 |
| concepts[7].score | 0.47439053654670715 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q15731404 |
| concepts[7].display_name | Malware analysis |
| concepts[8].id | https://openalex.org/C108583219 |
| concepts[8].level | 2 |
| concepts[8].score | 0.43119704723358154 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q197536 |
| concepts[8].display_name | Deep learning |
| concepts[9].id | https://openalex.org/C38652104 |
| concepts[9].level | 1 |
| concepts[9].score | 0.4272228479385376 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q3510521 |
| concepts[9].display_name | Computer security |
| concepts[10].id | https://openalex.org/C124101348 |
| concepts[10].level | 1 |
| concepts[10].score | 0.32141736149787903 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[10].display_name | Data mining |
| concepts[11].id | https://openalex.org/C144024400 |
| concepts[11].level | 0 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21201 |
| concepts[11].display_name | Sociology |
| concepts[12].id | https://openalex.org/C36289849 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q34749 |
| concepts[12].display_name | Social science |
| keywords[0].id | https://openalex.org/keywords/malware |
| keywords[0].score | 0.9237269759178162 |
| keywords[0].display_name | Malware |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.7998418807983398 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/machine-learning |
| keywords[2].score | 0.6202881336212158 |
| keywords[2].display_name | Machine learning |
| keywords[3].id | https://openalex.org/keywords/feature-engineering |
| keywords[3].score | 0.6105398535728455 |
| keywords[3].display_name | Feature engineering |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.6038026809692383 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/cryptovirology |
| keywords[5].score | 0.5272069573402405 |
| keywords[5].display_name | Cryptovirology |
| keywords[6].id | https://openalex.org/keywords/compromise |
| keywords[6].score | 0.5070266723632812 |
| keywords[6].display_name | Compromise |
| keywords[7].id | https://openalex.org/keywords/malware-analysis |
| keywords[7].score | 0.47439053654670715 |
| keywords[7].display_name | Malware analysis |
| keywords[8].id | https://openalex.org/keywords/deep-learning |
| keywords[8].score | 0.43119704723358154 |
| keywords[8].display_name | Deep learning |
| keywords[9].id | https://openalex.org/keywords/computer-security |
| keywords[9].score | 0.4272228479385376 |
| keywords[9].display_name | Computer security |
| keywords[10].id | https://openalex.org/keywords/data-mining |
| keywords[10].score | 0.32141736149787903 |
| keywords[10].display_name | Data mining |
| language | en |
| locations[0].id | doi:10.3390/jcp2040041 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210232532 |
| locations[0].source.issn | 2624-800X |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2624-800X |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Journal of Cybersecurity and Privacy |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623 |
| 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 Cybersecurity and Privacy |
| locations[0].landing_page_url | https://doi.org/10.3390/jcp2040041 |
| locations[1].id | pmh:oai:doaj.org/article:40b812d023b140ebb32c60d06a03d907 |
| locations[1].is_oa | True |
| 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 | cc-by-sa |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Journal of Cybersecurity and Privacy, Vol 2, Iss 4, Pp 800-829 (2022) |
| locations[1].landing_page_url | https://doaj.org/article/40b812d023b140ebb32c60d06a03d907 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5038560159 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Umm-e-Hani Tayyab |
| authorships[0].countries | PK |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I134276161 |
| authorships[0].affiliations[0].raw_affiliation_string | CIPMA Lab, DCIS, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[0].institutions[0].id | https://openalex.org/I134276161 |
| authorships[0].institutions[0].ror | https://ror.org/04d4mbk19 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I134276161 |
| authorships[0].institutions[0].country_code | PK |
| authorships[0].institutions[0].display_name | Pakistan Institute of Engineering and Applied Sciences |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Umm-e-Hani Tayyab |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | CIPMA Lab, DCIS, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[1].author.id | https://openalex.org/A5004231827 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-6751-8360 |
| authorships[1].author.display_name | Faiza Babar Khan |
| authorships[1].countries | PK |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I134276161 |
| authorships[1].affiliations[0].raw_affiliation_string | CIPMA Lab, DCIS, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[1].institutions[0].id | https://openalex.org/I134276161 |
| authorships[1].institutions[0].ror | https://ror.org/04d4mbk19 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I134276161 |
| authorships[1].institutions[0].country_code | PK |
| authorships[1].institutions[0].display_name | Pakistan Institute of Engineering and Applied Sciences |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Faiza Babar Khan |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | CIPMA Lab, DCIS, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[2].author.id | https://openalex.org/A5021414760 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-8026-1045 |
| authorships[2].author.display_name | Muhammad Hanif Durad |
| authorships[2].countries | PK |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I134276161 |
| authorships[2].affiliations[0].raw_affiliation_string | CIPMA Lab, DCIS, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[2].institutions[0].id | https://openalex.org/I134276161 |
| authorships[2].institutions[0].ror | https://ror.org/04d4mbk19 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I134276161 |
| authorships[2].institutions[0].country_code | PK |
| authorships[2].institutions[0].display_name | Pakistan Institute of Engineering and Applied Sciences |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Muhammad Hanif Durad |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | CIPMA Lab, DCIS, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[3].author.id | https://openalex.org/A5083112369 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-2039-5305 |
| authorships[3].author.display_name | Asifullah Khan |
| authorships[3].countries | PK |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I134276161 |
| authorships[3].affiliations[0].raw_affiliation_string | PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I134276161 |
| authorships[3].affiliations[1].raw_affiliation_string | Pattern Recognition Lab (PRLab), Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I134276161 |
| authorships[3].affiliations[2].raw_affiliation_string | Deep Learning Lab, Center for Mathematical Sciences (CMS), Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[3].institutions[0].id | https://openalex.org/I134276161 |
| authorships[3].institutions[0].ror | https://ror.org/04d4mbk19 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I134276161 |
| authorships[3].institutions[0].country_code | PK |
| authorships[3].institutions[0].display_name | Pakistan Institute of Engineering and Applied Sciences |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Asifullah Khan |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Deep Learning Lab, Center for Mathematical Sciences (CMS), Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan, PIEAS Artificial Intelligence Center (PAIC), Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan, Pattern Recognition Lab (PRLab), Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences, Nilore, Islamabad 45650, Pakistan |
| authorships[4].author.id | https://openalex.org/A5055136874 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6979-9952 |
| authorships[4].author.display_name | Yeon Soo Lee |
| authorships[4].countries | KR |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I39705031 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Biomedical Engineering, College of Medical Science, Catholic University of Daegu Hayangro, 13-13, Hayang-Eup, Gyoungsan-si 38430, Gyoungsangbuk-do, Korea |
| authorships[4].institutions[0].id | https://openalex.org/I39705031 |
| authorships[4].institutions[0].ror | https://ror.org/04fxknd68 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I39705031 |
| authorships[4].institutions[0].country_code | KR |
| authorships[4].institutions[0].display_name | Catholic University of Daegu |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Yeon Soo Lee |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | Department of Biomedical Engineering, College of Medical Science, Catholic University of Daegu Hayangro, 13-13, Hayang-Eup, Gyoungsan-si 38430, Gyoungsangbuk-do, Korea |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A Survey of the Recent Trends in Deep Learning Based Malware Detection |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11241 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 1.0 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1711 |
| primary_topic.subfield.display_name | Signal Processing |
| primary_topic.display_name | Advanced Malware Detection Techniques |
| related_works | https://openalex.org/W2469507153, https://openalex.org/W2008790809, https://openalex.org/W2160963033, https://openalex.org/W3022706011, https://openalex.org/W2909615516, https://openalex.org/W2768892939, https://openalex.org/W2249256574, https://openalex.org/W2397240470, https://openalex.org/W4210907385, https://openalex.org/W2065339563 |
| cited_by_count | 78 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 20 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 28 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 26 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 4 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/jcp2040041 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210232532 |
| best_oa_location.source.issn | 2624-800X |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2624-800X |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Journal of Cybersecurity and Privacy |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623 |
| 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 Cybersecurity and Privacy |
| best_oa_location.landing_page_url | https://doi.org/10.3390/jcp2040041 |
| primary_location.id | doi:10.3390/jcp2040041 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210232532 |
| primary_location.source.issn | 2624-800X |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2624-800X |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Journal of Cybersecurity and Privacy |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/2624-800X/2/4/41/pdf?version=1665674623 |
| 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 Cybersecurity and Privacy |
| primary_location.landing_page_url | https://doi.org/10.3390/jcp2040041 |
| publication_date | 2022-09-28 |
| publication_year | 2022 |
| referenced_works | https://openalex.org/W6806400966, https://openalex.org/W4214733430, https://openalex.org/W6751899208, https://openalex.org/W2105773843, https://openalex.org/W3016270591, https://openalex.org/W3016473151, https://openalex.org/W2998074434, https://openalex.org/W2784097977, https://openalex.org/W2900633536, https://openalex.org/W2921573932, https://openalex.org/W2929803724, https://openalex.org/W3048788384, https://openalex.org/W1544837488, https://openalex.org/W36091977, https://openalex.org/W2626060484, https://openalex.org/W1971139551, https://openalex.org/W2067724436, https://openalex.org/W1985663105, https://openalex.org/W3006333400, https://openalex.org/W2056729444, https://openalex.org/W2127969088, https://openalex.org/W2016975161, https://openalex.org/W568760110, https://openalex.org/W1993370323, https://openalex.org/W2294803890, https://openalex.org/W2182774916, https://openalex.org/W1584505081, https://openalex.org/W2742475488, https://openalex.org/W2246811656, https://openalex.org/W2289955225, https://openalex.org/W6702776042, https://openalex.org/W1966948031, https://openalex.org/W4292381820, https://openalex.org/W2747715470, https://openalex.org/W2557513839, https://openalex.org/W2792450155, https://openalex.org/W2796394805, https://openalex.org/W2801888526, https://openalex.org/W2803920557, https://openalex.org/W2734713605, https://openalex.org/W2914373984, https://openalex.org/W2508015754, https://openalex.org/W2931858311, https://openalex.org/W1666731339, https://openalex.org/W1893133781, https://openalex.org/W2907890159, https://openalex.org/W2752241832, https://openalex.org/W6804907660, https://openalex.org/W6804681350, https://openalex.org/W2969422541, https://openalex.org/W2944580495, https://openalex.org/W2981091784, https://openalex.org/W3000318628, https://openalex.org/W3019875810, https://openalex.org/W2990939506, https://openalex.org/W2011009207, https://openalex.org/W2247776437, https://openalex.org/W2252815174, https://openalex.org/W1824544070, https://openalex.org/W2236938640, https://openalex.org/W6697196120, https://openalex.org/W1981221397, https://openalex.org/W191656338, https://openalex.org/W2079215333, https://openalex.org/W2126401948, https://openalex.org/W1996975221, https://openalex.org/W1981229864, https://openalex.org/W1566259290, https://openalex.org/W2547412615, https://openalex.org/W3127627101, https://openalex.org/W3129906305, https://openalex.org/W2039427951, https://openalex.org/W3097711322, https://openalex.org/W2170060345, https://openalex.org/W139372241, https://openalex.org/W2910121883, https://openalex.org/W2895143461, https://openalex.org/W2954508354, https://openalex.org/W3102135219, https://openalex.org/W4205839607, https://openalex.org/W3100321043, https://openalex.org/W2804073276, https://openalex.org/W2295755339, https://openalex.org/W3215786562, https://openalex.org/W3099388751, https://openalex.org/W2334331178, https://openalex.org/W4200523179 |
| referenced_works_count | 87 |
| abstract_inverted_index.A | 247 |
| abstract_inverted_index.a | 18, 54, 210, 236 |
| abstract_inverted_index.In | 77, 219 |
| abstract_inverted_index.It | 171 |
| abstract_inverted_index.We | 294 |
| abstract_inverted_index.as | 147 |
| abstract_inverted_index.at | 215 |
| abstract_inverted_index.be | 60, 203 |
| abstract_inverted_index.by | 139, 269 |
| abstract_inverted_index.in | 100, 245, 250, 313 |
| abstract_inverted_index.is | 253 |
| abstract_inverted_index.of | 2, 33, 50, 74, 111, 157, 166, 176, 187, 213, 232, 257, 276, 284 |
| abstract_inverted_index.on | 142, 254 |
| abstract_inverted_index.or | 21, 72, 243 |
| abstract_inverted_index.so | 198 |
| abstract_inverted_index.to | 6, 17, 59, 80, 91, 102, 135, 149, 160, 162, 173, 192, 227, 239 |
| abstract_inverted_index.we | 222 |
| abstract_inverted_index.For | 44 |
| abstract_inverted_index.all | 40 |
| abstract_inverted_index.and | 26, 35, 234, 290, 305 |
| abstract_inverted_index.are | 98 |
| abstract_inverted_index.but | 39, 132 |
| abstract_inverted_index.can | 202 |
| abstract_inverted_index.few | 195 |
| abstract_inverted_index.for | 9, 30, 69, 180, 205, 217 |
| abstract_inverted_index.key | 248 |
| abstract_inverted_index.led | 172 |
| abstract_inverted_index.may | 130 |
| abstract_inverted_index.not | 67, 133 |
| abstract_inverted_index.the | 31, 78, 84, 109, 136, 143, 150, 163, 174, 184, 229, 255, 274, 282, 314 |
| abstract_inverted_index.use | 49, 175, 256 |
| abstract_inverted_index.Deep | 258, 261, 296 |
| abstract_inverted_index.They | 129 |
| abstract_inverted_index.With | 183 |
| abstract_inverted_index.also | 124 |
| abstract_inverted_index.area | 268 |
| abstract_inverted_index.come | 125 |
| abstract_inverted_index.data | 22, 158, 167, 214, 288 |
| abstract_inverted_index.deep | 177 |
| abstract_inverted_index.does | 66 |
| abstract_inverted_index.even | 209 |
| abstract_inverted_index.from | 88, 286 |
| abstract_inverted_index.hand | 216 |
| abstract_inverted_index.have | 41 |
| abstract_inverted_index.lead | 16 |
| abstract_inverted_index.make | 48 |
| abstract_inverted_index.shot | 196 |
| abstract_inverted_index.such | 57 |
| abstract_inverted_index.that | 199 |
| abstract_inverted_index.this | 64, 220, 251, 266 |
| abstract_inverted_index.with | 126, 194, 208, 278, 299 |
| abstract_inverted_index.work | 68 |
| abstract_inverted_index.(IOC) | 4 |
| abstract_inverted_index.IOCs, | 38 |
| abstract_inverted_index.based | 263, 303 |
| abstract_inverted_index.cater | 161 |
| abstract_inverted_index.chose | 191 |
| abstract_inverted_index.exist | 29 |
| abstract_inverted_index.fight | 81 |
| abstract_inverted_index.focus | 249 |
| abstract_inverted_index.large | 279 |
| abstract_inverted_index.leads | 5 |
| abstract_inverted_index.model | 238 |
| abstract_inverted_index.moved | 113 |
| abstract_inverted_index.phase | 141 |
| abstract_inverted_index.quest | 79 |
| abstract_inverted_index.small | 211 |
| abstract_inverted_index.tools | 25 |
| abstract_inverted_index.Hence, | 108 |
| abstract_inverted_index.amount | 212 |
| abstract_inverted_index.based) | 309 |
| abstract_inverted_index.breach | 20 |
| abstract_inverted_index.entire | 144 |
| abstract_inverted_index.events | 242 |
| abstract_inverted_index.layers | 156 |
| abstract_inverted_index.mining | 283 |
| abstract_inverted_index.paper, | 221 |
| abstract_inverted_index.speedy | 185 |
| abstract_inverted_index.survey | 252 |
| abstract_inverted_index.system | 19 |
| abstract_inverted_index.volume | 165 |
| abstract_inverted_index.Various | 24 |
| abstract_inverted_index.against | 105 |
| abstract_inverted_index.attacks | 71 |
| abstract_inverted_index.certain | 127 |
| abstract_inverted_index.classic | 117 |
| abstract_inverted_index.compare | 295 |
| abstract_inverted_index.dataset | 146 |
| abstract_inverted_index.dealing | 277 |
| abstract_inverted_index.delays. | 170 |
| abstract_inverted_index.feature | 272 |
| abstract_inverted_index.further | 169 |
| abstract_inverted_index.include | 131 |
| abstract_inverted_index.limited | 99, 134, 287 |
| abstract_inverted_index.machine | 93, 118, 121, 301 |
| abstract_inverted_index.malware | 7, 34, 51, 181, 206, 233 |
| abstract_inverted_index.methods | 90, 97, 115, 123, 179, 264, 310 |
| abstract_inverted_index.opposed | 148 |
| abstract_inverted_index.propose | 235 |
| abstract_inverted_index.several | 42, 224 |
| abstract_inverted_index.shifted | 87 |
| abstract_inverted_index.support | 228 |
| abstract_inverted_index.systems | 47 |
| abstract_inverted_index.threats | 244 |
| abstract_inverted_index.towards | 114 |
| abstract_inverted_index.Learning | 262 |
| abstract_inverted_index.analysis | 152, 308 |
| abstract_inverted_index.attacks, | 83 |
| abstract_inverted_index.attacks. | 107 |
| abstract_inverted_index.catering | 101 |
| abstract_inverted_index.commonly | 311 |
| abstract_inverted_index.database | 55 |
| abstract_inverted_index.discover | 240 |
| abstract_inverted_index.dominate | 265 |
| abstract_inverted_index.enabling | 281 |
| abstract_inverted_index.existing | 75 |
| abstract_inverted_index.features | 285 |
| abstract_inverted_index.however, | 120 |
| abstract_inverted_index.learning | 122, 197, 302 |
| abstract_inverted_index.malware, | 189 |
| abstract_inverted_index.malware. | 76 |
| abstract_inverted_index.methods. | 95, 260 |
| abstract_inverted_index.one-shot | 292 |
| abstract_inverted_index.paradigm | 86 |
| abstract_inverted_index.possible | 225 |
| abstract_inverted_index.produced | 204 |
| abstract_inverted_index.products | 28 |
| abstract_inverted_index.reliable | 200 |
| abstract_inverted_index.reported | 312 |
| abstract_inverted_index.research | 85, 112, 267 |
| abstract_inverted_index.samples, | 289 |
| abstract_inverted_index.security | 241 |
| abstract_inverted_index.surveyed | 223 |
| abstract_inverted_index.training | 145 |
| abstract_inverted_index.updated. | 62 |
| abstract_inverted_index.variants | 73 |
| abstract_inverted_index.zero-day | 70, 82, 106, 188 |
| abstract_inverted_index.Likewise, | 154 |
| abstract_inverted_index.Malicious | 13 |
| abstract_inverted_index.Primitive | 96 |
| abstract_inverted_index.activity. | 12 |
| abstract_inverted_index.automatic | 271 |
| abstract_inverted_index.classical | 92 |
| abstract_inverted_index.datasets, | 280 |
| abstract_inverted_index.detection | 8, 32, 207, 231 |
| abstract_inverted_index.direction | 110 |
| abstract_inverted_index.instance, | 45 |
| abstract_inverted_index.learning, | 119 |
| abstract_inverted_index.learning. | 293 |
| abstract_inverted_index.malicious | 11 |
| abstract_inverted_index.primitive | 89, 306 |
| abstract_inverted_index.providing | 270 |
| abstract_inverted_index.real-time | 151, 230 |
| abstract_inverted_index.requiring | 53 |
| abstract_inverted_index.solutions | 201 |
| abstract_inverted_index.technique | 65 |
| abstract_inverted_index.training. | 218 |
| abstract_inverted_index.utilizing | 37, 116 |
| abstract_inverted_index.Compromise | 3 |
| abstract_inverted_index.Indicators | 1 |
| abstract_inverted_index.Monitoring | 0 |
| abstract_inverted_index.activities | 14 |
| abstract_inverted_index.additional | 155 |
| abstract_inverted_index.approaches | 298, 304 |
| abstract_inverted_index.capability | 275 |
| abstract_inverted_index.constantly | 61 |
| abstract_inverted_index.containing | 56 |
| abstract_inverted_index.detection. | 182 |
| abstract_inverted_index.experiment | 193 |
| abstract_inverted_index.increasing | 164 |
| abstract_inverted_index.introduced | 138 |
| abstract_inverted_index.introduces | 168 |
| abstract_inverted_index.occurrence | 186 |
| abstract_inverted_index.real-time. | 246 |
| abstract_inverted_index.signatures | 58 |
| abstract_inverted_index.strategies | 226 |
| abstract_inverted_index.supporting | 291 |
| abstract_inverted_index.techniques | 104 |
| abstract_inverted_index.compromise. | 23 |
| abstract_inverted_index.engineering | 159 |
| abstract_inverted_index.identifying | 10 |
| abstract_inverted_index.latency/lag | 137 |
| abstract_inverted_index.literature. | 315 |
| abstract_inverted_index.potentially | 15 |
| abstract_inverted_index.researchers | 190 |
| abstract_inverted_index.signatures, | 52 |
| abstract_inverted_index.(statistical | 307 |
| abstract_inverted_index.anti-malware | 27, 46 |
| abstract_inverted_index.conventional | 300 |
| abstract_inverted_index.cyberattacks | 36 |
| abstract_inverted_index.engineering, | 273 |
| abstract_inverted_index.hierarchical | 237 |
| abstract_inverted_index.limitations. | 128 |
| abstract_inverted_index.requirement. | 153 |
| abstract_inverted_index.Additionally, | 63 |
| abstract_inverted_index.anti-analysis | 103 |
| abstract_inverted_index.shortcomings. | 43 |
| abstract_inverted_index.Learning-based | 259, 297 |
| abstract_inverted_index.learning-based | 94, 178 |
| abstract_inverted_index.feature-engineering | 140 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5083112369, https://openalex.org/A5055136874 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I134276161, https://openalex.org/I39705031 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5799999833106995 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.99477868 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |