Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review

Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to de...

Full description

Saved in:

Bibliographic Details
Published in:	Applied sciences Vol. 13; no. 21; p. 11908
Main Authors:	Gyamfi, Nana Kwame, Goranin, Nikolaj, Ceponis, Dainius, Čenys, Habil Antanas
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.11.2023
Subjects:	Algorithms Analysis anomaly detection Artificial intelligence automated system classification Automation Bibliometrics Classification Cybersecurity cybersecurity machine learning Cyberterrorism Decision trees Fuzzy logic Genetic algorithms Keywords Linear programming Literature reviews Machine learning Malware malware detection Neural networks Research methodology Spyware Support vector machines Systematic review Technology application
ISSN:	2076-3417, 2076-3417
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to detect zero-day attacks, polymorphic malware, increasing signature base, and detection speed. To achieve rapid detection, automated system-level malware detection using machine learning approaches, leveraging the power of artificial intelligence to identify and mitigate malware attacks, has emerged as a promising solution. This comprehensive review aims to provides a detailed analysis of the status quo in malware detection by exploring the fundamentals of machine learning techniques for malware detection. The review is largely based on the PRISMA approach for article search methods and selection from four databases. Keywords were identified together with inclusion and exclusion criteria. The review seeks feature extraction and selection methods that enhance the accuracy and precision of detection algorithms. Evaluation metrics and common datasets were used to assess the performance of the system-level malware detection techniques. A comparative analysis of different machine learning approaches, emphasizing their strengths, weaknesses, and performance in detecting system-level malware is presented together with the limitations of the detection techniques. The paper concludes with future research opportunities, particularly in applying artificial intelligence, and provides a resource for researchers and cybersecurity professionals seeking to understand and advance automated system-level malware detection using machine learning.
AbstractList	Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms dependent on signatures for attack detection perform poorly due to high false negatives. This limitation is attributed to the inability to detect zero-day attacks, polymorphic malware, increasing signature base, and detection speed. To achieve rapid detection, automated system-level malware detection using machine learning approaches, leveraging the power of artificial intelligence to identify and mitigate malware attacks, has emerged as a promising solution. This comprehensive review aims to provides a detailed analysis of the status quo in malware detection by exploring the fundamentals of machine learning techniques for malware detection. The review is largely based on the PRISMA approach for article search methods and selection from four databases. Keywords were identified together with inclusion and exclusion criteria. The review seeks feature extraction and selection methods that enhance the accuracy and precision of detection algorithms. Evaluation metrics and common datasets were used to assess the performance of the system-level malware detection techniques. A comparative analysis of different machine learning approaches, emphasizing their strengths, weaknesses, and performance in detecting system-level malware is presented together with the limitations of the detection techniques. The paper concludes with future research opportunities, particularly in applying artificial intelligence, and provides a resource for researchers and cybersecurity professionals seeking to understand and advance automated system-level malware detection using machine learning.
Audience	Academic
Author	Gyamfi, Nana Kwame Goranin, Nikolaj Ceponis, Dainius Čenys, Habil Antanas
Author_xml	– sequence: 1 givenname: Nana Kwame orcidid: 0000-0002-3249-4493 surname: Gyamfi fullname: Gyamfi, Nana Kwame – sequence: 2 givenname: Nikolaj orcidid: 0000-0002-2263-3947 surname: Goranin fullname: Goranin, Nikolaj – sequence: 3 givenname: Dainius orcidid: 0000-0002-2796-9001 surname: Ceponis fullname: Ceponis, Dainius – sequence: 4 givenname: Habil Antanas orcidid: 0000-0002-0208-7176 surname: Čenys fullname: Čenys, Habil Antanas
BookMark	eNptUV1rFDEUDVLBWvvmDxjw1an5mnz4tqxfhRVBLT6GNLnZZpmZjEl2S_-9WVekiLmQGw7nnFzueY7O5jQDQi8JvmJM4zd2WQijhBCN1RN0TrEUPeNEnj16P0OXpexwO5owRfA5-rHa1zTZCr779lAqTP0GDjB2n-14bzN076CCqzHN3U2J87bh7i7O0G3A5rkBb7tVt07TkuEO5hIP0H2FQ4T7F-hpsGOByz_9At18eP99_anffPl4vV5tesexqL3HjHFPtZaBWmUHrzHVkgwgrGRUWyZupQtCce4HMehApJDSewFDIIEBYRfo-uTrk92ZJcfJ5geTbDS_gZS3xuYa3QiGY6awahe79ZxTb7XmzIXgguKghWher05eS04_91Cq2aV9ntv4hiql2moF4411dWJtbTONc0g1W9fKwxRdyyTEhq-kpAMjjB8Fr08Cl1MpGcLfMQk2x-jM4-ganf5Dd7HaYwTtnzj-X_QLe_mbmA
CitedBy_id	crossref_primary_10_3390_app14114645 crossref_primary_10_3390_app15179830 crossref_primary_10_1051_e3sconf_202455601008 crossref_primary_10_48084_etasr_10420 crossref_primary_10_3390_info15010025 crossref_primary_10_3390_info16060458
Cites_doi	10.1016/j.eswa.2016.07.036 10.1109/ICSCN.2008.4447167 10.1016/j.ifacol.2015.09.589 10.1017/S0373463316000850 10.1007/s10994-021-06047-x 10.1007/s11227-009-0338-x 10.36001/phme.2014.v2i1.1554 10.1109/UBMK50275.2020.9219391 10.1109/CLOUD53861.2021.00043 10.1109/TCE.2015.7150594 10.1109/ICTAI.2019.00028 10.1016/j.future.2007.05.003 10.1155/2021/2459628 10.1109/CICYBS.2011.5949392 10.1109/ICODSE.2017.8285847 10.1016/j.cose.2011.08.009 10.1016/j.agrformet.2017.04.011 10.1109/IJCNN.2014.6889841 10.1007/978-3-642-40409-2_19 10.1109/MINES.2012.146 10.1109/ICDCSW.2013.40 10.1109/SAI.2014.6918242 10.1109/SPACES.2015.7058223 10.1109/ICONSIP.2016.7857445 10.1016/S0167-4048(03)00112-3 10.1109/ICPECTS.2018.8521623 10.1109/45.969588 10.1109/ICDM.2018.00088 10.1016/j.eswa.2010.08.137 10.1016/j.ymssp.2019.106495 10.1109/ICTEE.2012.6208614 10.1007/978-3-030-65414-6_11 10.1021/acs.jctc.5b01157 10.1016/j.mex.2019.100777 10.1109/CC.2014.6880464 10.1016/j.eswa.2017.09.013 10.1109/GLOCOMW.2015.7414141
ContentType	Journal Article
Copyright	COPYRIGHT 2023 MDPI AG 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI PRINS DOA
DOI	10.3390/app132111908
DatabaseName	CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Academic UKI Edition ProQuest Central Korea ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database CrossRef
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Engineering Sciences (General)
EISSN	2076-3417
ExternalDocumentID	oai_doaj_org_article_4038080383bd442da9943cffcf84e966 A772531344 10_3390_app132111908
GroupedDBID	.4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c406t-d0334d2997f2a8a5d9029715e6a7329a36b7cf6844d5659f17677dd6e5f1f3e13
IEDL.DBID	DOA
ISICitedReferencesCount	7
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001100279900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	2076-3417
IngestDate	Tue Oct 14 19:05:29 EDT 2025 Mon Jun 30 05:29:41 EDT 2025 Tue Nov 04 18:38:20 EST 2025 Sat Nov 29 07:18:50 EST 2025 Tue Nov 18 21:59:34 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	21
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c406t-d0334d2997f2a8a5d9029715e6a7329a36b7cf6844d5659f17677dd6e5f1f3e13
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-3249-4493 0000-0002-2796-9001 0000-0002-0208-7176 0000-0002-2263-3947
OpenAccessLink	https://doaj.org/article/4038080383bd442da9943cffcf84e966
PQID	2888111634
PQPubID	2032433
ParticipantIDs	doaj_primary_oai_doaj_org_article_4038080383bd442da9943cffcf84e966 proquest_journals_2888111634 gale_infotracacademiconefile_A772531344 crossref_primary_10_3390_app132111908 crossref_citationtrail_10_3390_app132111908
PublicationCentury	2000
PublicationDate	2023-11-01
PublicationDateYYYYMMDD	2023-11-01
PublicationDate_xml	– month: 11 year: 2023 text: 2023-11-01 day: 01
PublicationDecade	2020
PublicationPlace	Basel
PublicationPlace_xml	– name: Basel
PublicationTitle	Applied sciences
PublicationYear	2023
Publisher	MDPI AG
Publisher_xml	– name: MDPI AG
References	Lakshminarasimhan (ref_18) 2001; 20 Chen (ref_35) 2016; 12 ref_14 Vargaftik (ref_17) 2021; 110 ref_13 Forestiero (ref_39) 2008; 24 ref_11 Sarmadi (ref_12) 2019; 140 ref_33 Sukhwani (ref_36) 2014; 93 ref_10 ref_32 Yang (ref_29) 2021; 2021 ref_31 ref_30 Kolias (ref_37) 2011; 30 Mengist (ref_1) 2020; 7 ref_16 ref_15 Su (ref_44) 2011; 38 Abadi (ref_38) 2006; 2 Franco (ref_40) 2016; 12 Swarnkar (ref_4) 2016; 64 Puggini (ref_21) 2015; 48 ref_25 ref_23 ref_45 Zhen (ref_2) 2017; 70 ref_22 ref_20 ref_42 Cho (ref_34) 2003; 22 ref_3 Usman (ref_24) 2015; 61 ref_27 Yao (ref_6) 2017; 242 Salem (ref_9) 2017; 40 Yasami (ref_19) 2010; 53 ref_26 Wei (ref_43) 2014; 11 ref_8 ref_5 Hamamoto (ref_28) 2018; 92 ref_7 Goranin (ref_41) 2018; 6
References_xml	– volume: 64 start-page: 330 year: 2016 ident: ref_4 article-title: OCPAD: One class Naive Bayes classifier for payload based anomaly detection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.07.036 – ident: ref_22 doi: 10.1109/ICSCN.2008.4447167 – volume: 48 start-page: 583 year: 2015 ident: ref_21 article-title: Fault Detection using Random Forest Similarity Distance publication-title: IFAC-PapersOnLine doi: 10.1016/j.ifacol.2015.09.589 – volume: 93 start-page: 26 year: 2014 ident: ref_36 article-title: A Survey of Anomaly Detection Techniques and Hidden Markov Model publication-title: Int. J. Comput. Appl. – volume: 70 start-page: 648 year: 2017 ident: ref_2 article-title: Maritime Anomaly Detection within Coastal Waters Based on Vessel Trajectory Clustering and Naïve Bayes Classifier publication-title: J. Navig. doi: 10.1017/S0373463316000850 – volume: 110 start-page: 2835 year: 2021 ident: ref_17 article-title: RADE: Resource-efficient supervised anomaly detection using decision tree-based ensemble methods publication-title: Mach. Learn. doi: 10.1007/s10994-021-06047-x – volume: 53 start-page: 231 year: 2010 ident: ref_19 article-title: A novel unsupervised classification approach for network anomaly detection by k-means clustering and id3 decision tree learning methods publication-title: J. Supercomput. doi: 10.1007/s11227-009-0338-x – ident: ref_10 doi: 10.36001/phme.2014.v2i1.1554 – ident: ref_11 – ident: ref_7 doi: 10.1109/UBMK50275.2020.9219391 – volume: 40 start-page: 75 year: 2017 ident: ref_9 article-title: Brain tumor type classification based on support vector machine in magnetic resonance images publication-title: Ann. “Dunarea De Jos” Univ. Galati Math. Phys. Theor. Mech. Fascicle II – ident: ref_27 doi: 10.1109/CLOUD53861.2021.00043 – volume: 61 start-page: 197 year: 2015 ident: ref_24 article-title: Mobile agent-based cross-layer anomaly detection in smart home sensor networks using fuzzy logic publication-title: IEEE Trans. Consum. Electron. doi: 10.1109/TCE.2015.7150594 – volume: 6 start-page: 217 year: 2018 ident: ref_41 article-title: Towards a Robust Method of Dataset Generation of Malicious Activity for Anomaly-Based HIDS Training and Presentation of AWSCTD Dataset publication-title: Balt. J. Mod. Comput. – ident: ref_31 doi: 10.1109/ICTAI.2019.00028 – volume: 24 start-page: 235 year: 2008 ident: ref_39 article-title: QoS-based dissemination of content in Grids publication-title: Futur. Gener. Comput. Syst. doi: 10.1016/j.future.2007.05.003 – volume: 2021 start-page: 1 year: 2021 ident: ref_29 article-title: An anomaly detection algorithm selection service for IoT stream data based on fresh tool and genetic algorithm publication-title: Secur. Commun. Netw. doi: 10.1155/2021/2459628 – ident: ref_26 doi: 10.1109/CICYBS.2011.5949392 – ident: ref_20 doi: 10.1109/ICODSE.2017.8285847 – volume: 30 start-page: 625 year: 2011 ident: ref_37 article-title: Swarm intelligence in intrusion detection: A survey publication-title: Comput. Secur. doi: 10.1016/j.cose.2011.08.009 – volume: 242 start-page: 55 year: 2017 ident: ref_6 article-title: Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms publication-title: Agric. For. Meteorol. doi: 10.1016/j.agrformet.2017.04.011 – volume: 2 start-page: 106 year: 2006 ident: ref_38 article-title: An ant colony optimization algorithm for network vulnerability analysis publication-title: Iran. J. Electr. Electron. Eng. – ident: ref_5 doi: 10.1109/IJCNN.2014.6889841 – ident: ref_25 doi: 10.1007/978-3-642-40409-2_19 – ident: ref_23 doi: 10.1109/MINES.2012.146 – ident: ref_14 doi: 10.1109/ICDCSW.2013.40 – ident: ref_30 doi: 10.1109/SAI.2014.6918242 – ident: ref_8 – ident: ref_13 doi: 10.1109/SPACES.2015.7058223 – ident: ref_16 doi: 10.1109/ICONSIP.2016.7857445 – volume: 22 start-page: 45 year: 2003 ident: ref_34 article-title: Efficient anomaly detection by modeling privilege flows using hidden Markov model publication-title: Comput. Secur. doi: 10.1016/S0167-4048(03)00112-3 – ident: ref_15 doi: 10.1109/ICPECTS.2018.8521623 – volume: 20 start-page: 16 year: 2001 ident: ref_18 article-title: Computer viruses publication-title: IEEE Potentials doi: 10.1109/45.969588 – ident: ref_33 doi: 10.1109/ICDM.2018.00088 – volume: 38 start-page: 3492 year: 2011 ident: ref_44 article-title: Real-time anomaly detection systems for Denial-of-Service attacks by weighted k-nearest-neighbor classifiers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.08.137 – volume: 140 start-page: 106495 year: 2019 ident: ref_12 article-title: A novel anomaly detection method based on adaptive Mahalanobis-squared distance and one-class kNN rule for structural health monitoring under environmental effects publication-title: Mech. Syst. Signal Process. doi: 10.1016/j.ymssp.2019.106495 – ident: ref_3 doi: 10.1109/ICTEE.2012.6208614 – ident: ref_32 doi: 10.1007/978-3-030-65414-6_11 – volume: 12 start-page: 3416 year: 2016 ident: ref_40 article-title: Firefly algorithm for structural search publication-title: J. Chem. Theory Comput. doi: 10.1021/acs.jctc.5b01157 – ident: ref_45 – volume: 7 start-page: 100777 year: 2020 ident: ref_1 article-title: Method for conducting systematic literature review and meta-analysis for environmental science research publication-title: MethodsX doi: 10.1016/j.mex.2019.100777 – volume: 11 start-page: 91 year: 2014 ident: ref_43 article-title: A digital evidence fusion method in network forensics systems with Dempster-shafer theory publication-title: China Commun. doi: 10.1109/CC.2014.6880464 – volume: 12 start-page: 569 year: 2016 ident: ref_35 article-title: Anomaly network intrusion detection using hidden Markov model publication-title: Int. J. Innov. Comput. Inf. Control – volume: 92 start-page: 390 year: 2018 ident: ref_28 article-title: Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.09.013 – ident: ref_42 doi: 10.1109/GLOCOMW.2015.7414141
SSID	ssj0000913810
Score	2.3113468
SecondaryResourceType	review_article
Snippet	Malware poses a significant threat to computer systems and networks. This necessitates the development of effective detection mechanisms. Detection mechanisms...
SourceID	doaj proquest gale crossref
SourceType	Open Website Aggregation Database Enrichment Source Index Database
StartPage	11908
SubjectTerms	Algorithms Analysis anomaly detection Artificial intelligence automated system classification Automation Bibliometrics Classification Cybersecurity cybersecurity machine learning Cyberterrorism Decision trees Fuzzy logic Genetic algorithms Keywords Linear programming Literature reviews Machine learning Malware malware detection Neural networks Research methodology Spyware Support vector machines Systematic review Technology application
SummonAdditionalLinks	– databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NT9RAFH_BxYMeRFDiKpo5SNCYhunOtNPhQhaBeIANMRq5TabzASZkF7tF_33fa2eXveDFaztppnmfv3lvfg_gPWbgRe2iyDz3ZYZ4w2e100UmaxW5toGHwnbDJtRkUl1e6ot04DZPbZULn9g5aj9zdEa-P0KohnZZCnl4-yujqVFUXU0jNB7BOjGVyQGsH51MLr4uT1mI9bLKed_xLhDfU10YARh-SdNEyZVY1FH2P-SYu2hzuvG_-3wOz1Keyca9YmzCWphuwdMV9sEt2Ex2PWcfEvn0xxfwY3zXzjCLDZ71ZObZGbUVsXN788c2gR2HtmvemrKu2QCfUzNmYImn9eqAjRn5mCZc963xrK8-vITvpyffPn_J0vCFzGGMb1F2QkiPwUrFka1s4TWNucqLUFolRtqKslYulpWUHnNCHXNVKuV9GYqYRxFysQ2D6WwaXgGzPqdiYcF5XUihfF1zF6tSOW4rBGh2CJ8WYjAuMZPTgIwbgwiFhGZWhTaE3eXq256R44F1RyTR5Rri0e4ezJork8zSSC6IWBNheu2lHHmrtRQuRtyfDIgEh7BH-mDI2nFLzqZLC_hjxJtlxghO0IsJKYews9AHk9zA3Nwrw-t_v34DT2iOfX_JcQcGbXMX3sJj97v9OW_eJa3-CxWB_2U priority: 102 providerName: ProQuest
Title	Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
URI	https://www.proquest.com/docview/2888111634 https://doaj.org/article/4038080383bd442da9943cffcf84e966
Volume	13
WOSCitedRecordID	wos001100279900001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LT9wwEB5V0AMcKqCt2Ha78qGorVDUZO3EMbelBRWprFZVEfRkOX7QSmi32s3C32cmNigX1EuPSXyYjOf1yeNvAN5jBV42NvDM5a7KEG-4rLGqzEQjQ66Mz31pumETcjqtr67UrDfqi3rCIj1wVNxnkXOiPkQg1Tghxs4oJbgNwYZaeKzVKfri9x6Y6mKwKoi6Kna6c8T1dB6MwAs9W9EkyV4O6qj6nwrIXZY53YEXqTxkkyjWLjzz8z3Y7pEG7sFucscV-5g4oz-9hMvJul1g8ekdixzk2XfqBmLn5ubOLD376tuu52rOuh4BfE89lJ4letXrIzZhFBqW_nfsaGfx0OAVXJye_PzyLUszEzKLqblFlXMuHOYYGcamNqVTNJ2qKH1lJB8rw6tG2lDVQjgs5VQoZCWlc5UvQxG4L_hr2Jgv5n4fmHEFnfGVed6UgkvXNDkqvZI2NzXiKjOAwwctapsIxWmuxY1GYEE6132dD-DgcfXfSKTxxLpj2pDHNUR_3b1Ao9DJKPS_jGIAH2g7NTkpimRNumuAP0Z0V3qCmAKDDxdiAMOHHdfJe1d6XNc1SlNx8eZ_SPMWtmhIfbzBOISNdrn27-C5vW3_rJYj2Dw-mc5-jDoDxqfZ2fns1z0hV_K_
linkProvider	Directory of Open Access Journals
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VFgk4AC0gFgr4QAUIRTixE8dICG1pq1bdripU1N6M44-CVO2WbErFn-I3Mk6cZS_l1gPXxIrs-Pl5xjN-A_ASLfC8Mp4lltoiQX_DJpWRecIr4anUjrpct8UmxHhcnpzIwyX43d-FCWmVPSe2RG2nJpyRv8vQVcN1WTD-8fxHEqpGhehqX0Kjg8W--3WJLtvsw94Wzu9Glu1sH33aTWJVgcTg5tVgpxjjFllY-EyXOrcy1G9Kc1dowTKpWVEJ44uSc4vGjvSpKISwtnC5Tz1zKcPv3oAVzrjAdbWyuT0-_Dw_1Qkqm2VKuwx7xiQNcWh0-LDnMlSwXNj72hIBV20E7e62c-9_-y_34W60o8mwA_4qLLnJGtxZUFdcg9XIWzPyOoprv3kAx8OLZopWurOkE2tPRiFtihzos0tdO7LlmjY5bULaZAp8HpJNHYk6tKfvyZAEDq3dty71n3TRlYfw5VqG-wiWJ9OJewxE2zQEQ3NKq5wzYauKGl8WwlBdogOqB_C2n3ZlovJ6KAByptADCyBRiyAZwMa89XmnOHJFu82AoHmboBPePpjWpyrSjuKUBeFQVrLKcp5ZLSVnxnvsH3fo6Q7gVcCfCmyGXTI6XsrAgQVdMDVE5wtZmnE-gPUefyrS3Ez9Bd-Tf79-Abd2jw5GarQ33n8KtzO0FLsLneuw3NQX7hncND-b77P6eVxRBL5eN1j_ACOjWjc
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Nb9QwEB2VFiE4AC2gLhTwgQoQiurEThwjIbSwrFi1Xe0BRDkZxx8tUrVbsikVf41fxzjxLnsptx64OlZkJ8_PM57xG4BnaIHnlfEssdQWCfobNqmMzBNeCU-ldtTlui02Icbj8uhITtbg9-IuTEirXHBiS9R2ZsIZ-V6Grhquy4LxPR_TIiaD4duzH0moIBUirYtyGh1E9t2vC3Tf5m9GA_zXu1k2_PDp_cckVhhIDG5kDQ6QMW6RkYXPdKlzK0MtpzR3hRYsk5oVlTC-KDm3aPhIn4pCCGsLl_vUM5cyfO812ECTnOMa25iMDidflyc8QXGzTGmXbc-YpCEmjc4fzkKGapYr-2BbLuCyTaHd6YZ3_udvdBduR_ua9LsFsQlrbroFt1ZUF7dgM_LZnLyIotsv78GX_nkzQ-vdWdKJuCcHIZ2KHOrTC107MnBNm7Q2JW2SBbaHJFRHoj7t8WvSJ4Fba3fSXQkgXdTlPny-kuk-gPXpbOq2gWibhiBpTmmVcyZsVVHjy0IYqkt0THUPXi0goExUZA-FQU4VemYBMGoVMD3YXfY-65RILun3LqBp2Sfoh7cNs_pYRTpSnLIgKMpKVlnOM6ul5Mx4j-PjDj3gHjwPWFSB5XBIRsfLGjixoBem-uiUIXszznuws8CiivQ3V3-B-PDfj5_CDUSoOhiN9x_BzQwNyO6e5w6sN_W5ewzXzc_m-7x-EhcXgW9XjdU_zFRjKQ
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Automated+System-Level+Malware+Detection+Using+Machine+Learning%3A+A+Comprehensive+Review&rft.jtitle=Applied+sciences&rft.au=Gyamfi%2C+Nana+Kwame&rft.au=Goranin%2C+Nikolaj&rft.au=Ceponis%2C+Dainius&rft.au=%C4%8Cenys%2C+Habil+Antanas&rft.date=2023-11-01&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=13&rft.issue=21&rft.spage=11908&rft_id=info:doi/10.3390%2Fapp132111908&rft.externalDBID=n%2Fa&rft.externalDocID=10_3390_app132111908
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon