Next-generation antivirus for JavaScript malware detection based on dynamic features

There are many kinds of Exploit Kits, each one being built with several vulnerabilities, but almost all of them are written in JavaScript. So, we created an antivirus, endowed with machine learning, expert in detecting JavaScript malware based on Runtime Behaviors. In our methodology, JavaScript is...

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Published in:	Knowledge and information systems Vol. 66; no. 2; pp. 1337 - 1370
Main Authors:	de Lima, Sidney M. L., Souza, Danilo M., Pinheiro, Ricardo P., Silva, Sthéfano H. M. T., Lopes, Petrônio G., de Lima, Rafael D. T., de Oliveira, Jemerson R., Monteiro, Thyago de A., Fernandes, Sérgio M. M., Albuquerque, Edison de Q., da Silva, Washington W. A., dos Santos, Wellington P.
Format:	Journal Article
Language:	English
Published:	London Springer London 01.02.2024 Springer Nature B.V
Subjects:	Accuracy Anti-virus software Computer Science Cybersecurity Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business JavaScript Machine learning Malware Operating systems Regular Paper Computer forensics Dynamic features JavaScript Machine learning Malware Antivirus Sandbox
ISSN:	0219-1377, 0219-3116
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Abstract	There are many kinds of Exploit Kits, each one being built with several vulnerabilities, but almost all of them are written in JavaScript. So, we created an antivirus, endowed with machine learning, expert in detecting JavaScript malware based on Runtime Behaviors. In our methodology, JavaScript is executed, in a controlled environment. The goal was to investigate suspicious file behavior. Our antivirus, as a whole, dynamically monitors and ponders 7690 suspicious behaviors that the JavaScript file can do in Windows 7. As experiments, the authorial antivirus is compared to antiviruses based on deep as based on shallow networks. Our antivirus achieves an average accuracy of 99.75% in the distinction between benign and malware, accompanied by a training time of 8.92 s. Establishing the relationship between accuracy and training time is essential in information security. Eight (8) new malware are released every second. An antivirus with excessive training time can become obsolete even when released. As our proposed model can overcome the limitations of state-of-the-art, our antivirus combines high accuracy and fast training. In addition, the authorial antivirus is able to detect JavaScript malware, endowed with digital antiforense, such as obfuscates, polymorphic and fileless attacks.
AbstractList	There are many kinds of Exploit Kits, each one being built with several vulnerabilities, but almost all of them are written in JavaScript. So, we created an antivirus, endowed with machine learning, expert in detecting JavaScript malware based on Runtime Behaviors. In our methodology, JavaScript is executed, in a controlled environment. The goal was to investigate suspicious file behavior. Our antivirus, as a whole, dynamically monitors and ponders 7690 suspicious behaviors that the JavaScript file can do in Windows 7. As experiments, the authorial antivirus is compared to antiviruses based on deep as based on shallow networks. Our antivirus achieves an average accuracy of 99.75% in the distinction between benign and malware, accompanied by a training time of 8.92 s. Establishing the relationship between accuracy and training time is essential in information security. Eight (8) new malware are released every second. An antivirus with excessive training time can become obsolete even when released. As our proposed model can overcome the limitations of state-of-the-art, our antivirus combines high accuracy and fast training. In addition, the authorial antivirus is able to detect JavaScript malware, endowed with digital antiforense, such as obfuscates, polymorphic and fileless attacks. There are many kinds of Exploit Kits, each one being built with several vulnerabilities, but almost all of them are written in JavaScript. So, we created an antivirus, endowed with machine learning, expert in detecting JavaScript malware based on Runtime Behaviors. In our methodology, JavaScript is executed, in a controlled environment. The goal was to investigate suspicious file behavior. Our antivirus, as a whole, dynamically monitors and ponders 7690 suspicious behaviors that the JavaScript file can do in Windows 7. As experiments, the authorial antivirus is compared to antiviruses based on deep as based on shallow networks. Our antivirus achieves an average accuracy of 99.75% in the distinction between benign and malware, accompanied by a training time of 8.92 s. Establishing the relationship between accuracy and training time is essential in information security. Eight (8) new malware are released every second. An antivirus with excessive training time can become obsolete even when released. As our proposed model can overcome the limitations of state-of-the-art, our antivirus combines high accuracy and fast training. In addition, the authorial antivirus is able to detect JavaScript malware, endowed with digital antiforense, such as obfuscates, polymorphic and fileless attacks.
Author	de Lima, Rafael D. T. dos Santos, Wellington P. da Silva, Washington W. A. Souza, Danilo M. Pinheiro, Ricardo P. Monteiro, Thyago de A. Fernandes, Sérgio M. M. Lopes, Petrônio G. de Oliveira, Jemerson R. de Lima, Sidney M. L. Silva, Sthéfano H. M. T. Albuquerque, Edison de Q.
Author_xml	– sequence: 1 givenname: Sidney M. L. orcidid: 0000-0002-4350-9689 surname: de Lima fullname: de Lima, Sidney M. L. email: sidney.lima@ufpe.br, smll@ecomp.poli.br organization: Electronics and Systems Department, Federal University of Pernambuco – sequence: 2 givenname: Danilo M. surname: Souza fullname: Souza, Danilo M. organization: Computing Department, University of Pernambuco – sequence: 3 givenname: Ricardo P. surname: Pinheiro fullname: Pinheiro, Ricardo P. organization: Computing Department, University of Pernambuco – sequence: 4 givenname: Sthéfano H. M. T. surname: Silva fullname: Silva, Sthéfano H. M. T. organization: Computing Department, University of Pernambuco – sequence: 5 givenname: Petrônio G. surname: Lopes fullname: Lopes, Petrônio G. organization: Computing Department, University of Pernambuco – sequence: 6 givenname: Rafael D. T. surname: de Lima fullname: de Lima, Rafael D. T. organization: Computing Department, University of Pernambuco – sequence: 7 givenname: Jemerson R. surname: de Oliveira fullname: de Oliveira, Jemerson R. organization: Computing Department, University of Pernambuco – sequence: 8 givenname: Thyago de A. surname: Monteiro fullname: Monteiro, Thyago de A. organization: Computing Department, University of Pernambuco – sequence: 9 givenname: Sérgio M. M. surname: Fernandes fullname: Fernandes, Sérgio M. M. organization: Computing Department, University of Pernambuco – sequence: 10 givenname: Edison de Q. surname: Albuquerque fullname: Albuquerque, Edison de Q. organization: Computing Department, University of Pernambuco – sequence: 11 givenname: Washington W. A. surname: da Silva fullname: da Silva, Washington W. A. organization: Biomedical Engineering Department, Federal University of Pernambuco – sequence: 12 givenname: Wellington P. surname: dos Santos fullname: dos Santos, Wellington P. organization: Biomedical Engineering Department, Federal University of Pernambuco
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CitedBy_id	crossref_primary_10_1016_j_asoc_2025_113537 crossref_primary_10_1007_s11416_024_00526_0 crossref_primary_10_1007_s11227_024_06551_6 crossref_primary_10_3389_fphy_2024_1349463
Cites_doi	10.1007/s10115-022-01707-3 10.1016/j.neucom.2018.10.063 10.1109/ICCWS53234.2021.9703021 10.1016/S1361-3723(14)70531-7 10.1016/j.ins.2011.09.016 10.1016/j.cmpb.2016.04.029 10.1007/s10115-022-01672-x 10.1007/s10115-023-01906-6 10.1007/s10115-023-01860-3 10.1007/978-981-13-7561-3_10 10.1109/RDCAPE.2017.8358312 10.1016/j.jnca.2012.10.004 10.1007/s13748-020-00220-4 10.1109/TSMCB.2011.2168604 10.1007/s10115-022-01753-x 10.1109/72.846750 10.1109/SYNASC.2014.39 10.4018/978-1-7998-5728-0.ch020 10.1109/SMC.2014.6974041 10.1109/IJCNN.2015.7280774 10.1016/B978-0-12-819295-5.00003-2 10.1007/978-3-319-47121-1_5 10.1109/COMPSAC.2018.10315 10.1109/CVPR.2017.195 10.1109/FUZZ-IEEE.2015.7337975
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References	Salehi, Sami, Ghiasi (CR26) 2014; 9 Shahzad, Shahzad (CR30) 2013; 231 Huang (CR15) 2012; 42 Santos, Silva Filho, Santos (CR29) 2019; 329 CR19 CR18 Faruki, Buddhadev (CR10) 2019 CR16 Santos (CR28) 2011 Ferreira, Silva, Itzazelaia (CR11) 2023 CR13 Maniath, Ashok (CR23) 2017 CR12 Huang (CR14) 2000; 11 CR34 CR33 CR32 Alam, Akram, Saeed, Arshad (CR1) 2021; 2021 Lima, Silva-Filho, Santos (CR21) 2016; 134 CR2 CR4 CR3 CR8 CR7 Lima, Silva, Luz (CR22) 2020 CR9 CR27 CR25 SIKOS (CR31) 2023; 65 CR24 Challapalli, Devarakonda (CR6) 2022; 64 CR20 Wang, Zhang, Guo, Zhao (CR35) 2023; 65 Islam, Tian, Batten, Versteeg (CR17) 2013; 36 Catal, Giray, Tekinerdogan, Kumar, SHUKLA (CR5) 2022; 64 Z Salehi (1978_CR26) 2014; 9 P Faruki (1978_CR10) 2019 GB Huang (1978_CR15) 2012; 42 1978_CR7 S Lima (1978_CR22) 2020 1978_CR8 WP Santos (1978_CR28) 2011 1978_CR9 L SIKOS (1978_CR31) 2023; 65 1978_CR18 1978_CR19 1978_CR2 1978_CR3 1978_CR16 S Maniath (1978_CR23) 2017 1978_CR4 1978_CR32 MM Santos (1978_CR29) 2019; 329 1978_CR33 J Challapalli (1978_CR6) 2022; 64 1978_CR12 1978_CR34 1978_CR13 F Shahzad (1978_CR30) 2013; 231 X Wang (1978_CR35) 2023; 65 C Catal (1978_CR5) 2022; 64 S Lima (1978_CR21) 2016; 134 M Alam (1978_CR1) 2021; 2021 GB Huang (1978_CR14) 2000; 11 L Ferreira (1978_CR11) 2023 1978_CR25 1978_CR27 1978_CR24 R Islam (1978_CR17) 2013; 36 1978_CR20
References_xml	– ident: CR18 – ident: CR4 – ident: CR2 – ident: CR16 – ident: CR12 – volume: 64 start-page: 2411 year: 2022 end-page: 2434 ident: CR6 article-title: A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of indian classical dances publication-title: Knowl Inf Syst doi: 10.1007/s10115-022-01707-3 – volume: 329 start-page: 359 year: 2019 end-page: 369 ident: CR29 article-title: Deep convolutional extreme learning machines: filters combination and error model validation publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.10.063 – volume: 2021 start-page: 93 year: 2021 end-page: 99 ident: CR1 article-title: Deepmalware: a deep learning based malware images classification publication-title: Int Conf Cyber Warfare Secur doi: 10.1109/ICCWS53234.2021.9703021 – ident: CR33 – ident: CR8 – volume: 9 start-page: 9 year: 2014 end-page: 18 ident: CR26 article-title: Using features generation from API calls form malware detection publication-title: Comput Fraud Secur doi: 10.1016/S1361-3723(14)70531-7 – ident: CR25 – ident: CR27 – volume: 231 start-page: 45 year: 2013 end-page: 63 ident: CR30 article-title: In-execution dynamic malware analysis and detection by mining information in process control blocks of linux os publication-title: Inf Sci doi: 10.1016/j.ins.2011.09.016 – volume: 134 start-page: 11 year: 2016 end-page: 29 ident: CR21 article-title: Detection and classification of masses in mammographic images in a multi-kernel approach publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2016.04.029 – volume: 64 start-page: 1457 year: 2022 end-page: 1500 ident: CR5 article-title: Applications of deep learning for phishing detection: a systematic literature review publication-title: Knowl Inf Syst doi: 10.1007/s10115-022-01672-x – ident: CR19 – year: 2023 ident: CR11 article-title: Recommender systems in cybersecurity publication-title: Knowl Inf Syst doi: 10.1007/s10115-023-01906-6 – volume: 65 start-page: 3511 year: 2023 end-page: 3531 ident: CR31 article-title: Cybersecurity knowledge graphs publication-title: Knowl Inf Syst doi: 10.1007/s10115-023-01860-3 – ident: CR3 – ident: CR13 – year: 2019 ident: CR10 article-title: Droiddivesdeep: android malware classification via low level monitorable features with deep neural networks publication-title: Int Conf Secur Priv doi: 10.1007/978-981-13-7561-3_10 – ident: CR9 – year: 2017 ident: CR23 article-title: Deep learning lstm based ransomware detection publication-title: Recent Dev Contr Autom Power Eng doi: 10.1109/RDCAPE.2017.8358312 – volume: 36 start-page: 646 year: 2013 end-page: 656 ident: CR17 article-title: Classification of malware based on integrated static and dynamic features publication-title: J Netw Comput Appl doi: 10.1016/j.jnca.2012.10.004 – ident: CR32 – ident: CR34 – year: 2020 ident: CR22 article-title: Artificial intelligence-based antivirus in order to detect malware preventively publication-title: Progr Artif Intelli doi: 10.1007/s13748-020-00220-4 – ident: CR7 – year: 2011 ident: CR28 publication-title: Mathematical morphology in digital document analysis and processing – volume: 42 start-page: 513 issue: 2 year: 2012 end-page: 519 ident: CR15 article-title: Extreme learning machine for regression and multiclass classification publication-title: IEEE Trans Syst Man Cybern doi: 10.1109/TSMCB.2011.2168604 – volume: 65 start-page: 945 year: 2023 end-page: 989 ident: CR35 article-title: A survey of continuous subgraph matching for dynamic graphs publication-title: Knowl Inf Syst doi: 10.1007/s10115-022-01753-x – ident: CR24 – volume: 11 start-page: 799 issue: 3 year: 2000 end-page: 801 ident: CR14 article-title: Classification ability of single hidden layer feedforward neural networks publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/72.846750 – ident: CR20 – volume: 134 start-page: 11 year: 2016 ident: 1978_CR21 publication-title: Comput Methods Progr Biomed doi: 10.1016/j.cmpb.2016.04.029 – volume: 9 start-page: 9 year: 2014 ident: 1978_CR26 publication-title: Comput Fraud Secur doi: 10.1016/S1361-3723(14)70531-7 – ident: 1978_CR27 – ident: 1978_CR9 doi: 10.1109/SYNASC.2014.39 – ident: 1978_CR18 doi: 10.4018/978-1-7998-5728-0.ch020 – ident: 1978_CR25 – ident: 1978_CR33 – volume: 36 start-page: 646 year: 2013 ident: 1978_CR17 publication-title: J Netw Comput Appl doi: 10.1016/j.jnca.2012.10.004 – ident: 1978_CR19 doi: 10.1109/SMC.2014.6974041 – volume-title: Mathematical morphology in digital document analysis and processing year: 2011 ident: 1978_CR28 – ident: 1978_CR3 doi: 10.1109/IJCNN.2015.7280774 – ident: 1978_CR4 doi: 10.1016/B978-0-12-819295-5.00003-2 – volume: 64 start-page: 1457 year: 2022 ident: 1978_CR5 publication-title: Knowl Inf Syst doi: 10.1007/s10115-022-01672-x – ident: 1978_CR16 – volume: 11 start-page: 799 issue: 3 year: 2000 ident: 1978_CR14 publication-title: IEEE Trans. Neural Netw. Learn. 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Snippet	There are many kinds of Exploit Kits, each one being built with several vulnerabilities, but almost all of them are written in JavaScript. So, we created an...
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SubjectTerms	Accuracy Anti-virus software Computer Science Cybersecurity Data Mining and Knowledge Discovery Database Management Information Storage and Retrieval Information Systems and Communication Service Information Systems Applications (incl.Internet) IT in Business JavaScript Machine learning Malware Operating systems Regular Paper
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Title	Next-generation antivirus for JavaScript malware detection based on dynamic features
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