Prediction of novel malware using hybrid convolution neural network and long short-term memory approach.
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| Title: | Prediction of novel malware using hybrid convolution neural network and long short-term memory approach. |
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| Authors: | Pachhala, Nagababu1 nagababupachhala2024@gmail.com, Jothilakshmi, Subbaiyan1 jothi.sekar@gmail.com, Battula, Bhanu Prakash2 prakashbattula33@gmail.com |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Aug2024, Vol. 14 Issue 4, p4508-4517. 10p. |
| Subjects: | Convolutional neural networks, Personal computer software, Data privacy, Antivirus software, Malware, Computer firmware |
| Abstract: | The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 178843340 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Prediction of novel malware using hybrid convolution neural network and long short-term memory approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Pachhala%2C+Nagababu%22">Pachhala, Nagababu</searchLink><relatesTo>1</relatesTo><i> nagababupachhala2024@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Jothilakshmi%2C+Subbaiyan%22">Jothilakshmi, Subbaiyan</searchLink><relatesTo>1</relatesTo><i> jothi.sekar@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Battula%2C+Bhanu+Prakash%22">Battula, Bhanu Prakash</searchLink><relatesTo>2</relatesTo><i> prakashbattula33@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Aug2024, Vol. 14 Issue 4, p4508-4517. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Personal+computer+software%22">Personal computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Data+privacy%22">Data privacy</searchLink><br /><searchLink fieldCode="DE" term="%22Antivirus+software%22">Antivirus software</searchLink><br /><searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+firmware%22">Computer firmware</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rapid evolution of network communication technologies has led to the emergence of new forms of malware and cybercrimes, posing significant threats to user safety, network infrastructure integrity, and data privacy. Despite efforts to develop advanced algorithms for detecting malicious activity, constructing models that are both accurate and reliable remains a challenge, especially in handling vast and dynamically shifting data patterns. The prevalent bag-of-words (BOW) method, while widely used, falls short in capturing crucial spatial and sequence information vital for detecting malware patterns. To address this challenge, the work presented in this paper proposes hybrid convolution neural network-long short-term memory network (CNN-LSTM) combination models, leveraging CNN's spatial information extraction and LSTM's temporal modeling capabilities. Focused on predicting the infiltration of malicious software into personal computers, the proposed hybrid CNN-LSTM model considers factors such as location, firmware version, operating system, and anti-virus software. The proposed models undergo training and evaluation using Microsoft's malware dataset, demonstrating superior performance compared to traditional CNN and LSTM models. The CNN-LSTM model achieves an impressive accuracy of 95% on the Microsoft malware dataset, highlighting its effectiveness in malware detection. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v14i4.pp4508-4517 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 4508 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Personal computer software Type: general – SubjectFull: Data privacy Type: general – SubjectFull: Antivirus software Type: general – SubjectFull: Malware Type: general – SubjectFull: Computer firmware Type: general Titles: – TitleFull: Prediction of novel malware using hybrid convolution neural network and long short-term memory approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Pachhala, Nagababu – PersonEntity: Name: NameFull: Jothilakshmi, Subbaiyan – PersonEntity: Name: NameFull: Battula, Bhanu Prakash IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 14 – Type: issue Value: 4 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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