Advances in Malware and Data-Driven Network Security
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| Title: | Advances in Malware and Data-Driven Network Security |
|---|---|
| Description: | Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security. |
| Authors: | Brij B. Gupta |
| Resource Type: | eBook. |
| Subjects: | Malware (Computer software), Computer networks--Security measures, Digital forensic science, Machine learning |
| Categories: | COMPUTERS / Security / Viruses & Malware, COMPUTERS / Security / Network Security, COMPUTERS / Security / General |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf – Type: ebook-epub Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Advances in Malware and Data-Driven Network Security – Name: Abstract Label: Description Group: Ab Data: Every day approximately three-hundred thousand to four-hundred thousand new malware are registered, many of them being adware and variants of previously known malware. Anti-virus companies and researchers cannot deal with such a deluge of malware – to analyze and build patches. The only way to scale the efforts is to build algorithms to enable machines to analyze malware and classify and cluster them to such a level of granularity that it will enable humans (or machines) to gain critical insights about them and build solutions that are specific enough to detect and thwart existing malware and generic-enough to thwart future variants. Advances in Malware and Data-Driven Network Security comprehensively covers data-driven malware security with an emphasis on using statistical, machine learning, and AI as well as the current trends in ML/statistical approaches to detecting, clustering, and classification of cyber-threats. Providing information on advances in malware and data-driven network security as well as future research directions, it is ideal for graduate students, academicians, faculty members, scientists, software developers, security analysts, computer engineers, programmers, IT specialists, and researchers who are seeking to learn and carry out research in the area of malware and data-driven network security. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Brij+B%2E+Gupta%22">Brij B. Gupta</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Malware+%28Computer+software%29%22">Malware (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+networks--Security+measures%22">Computer networks--Security measures</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+forensic+science%22">Digital forensic science</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Security+%2F+Viruses+%26+Malware%22">COMPUTERS / Security / Viruses & Malware</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Security+%2F+Network+Security%22">COMPUTERS / Security / Network Security</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Security+%2F+General%22">COMPUTERS / Security / General</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=3097366 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 005.8 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Malware (Computer software) Type: general – SubjectFull: Computer networks--Security measures Type: general – SubjectFull: Digital forensic science Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Advances in Malware and Data-Driven Network Security Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Brij B. Gupta – PersonEntity: Name: NameFull: Brij B. Gupta IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 – D: 04 M: 01 Type: profile Y: 2022 Identifiers: – Type: isbn-print Value: 9781799877899 – Type: isbn-electronic Value: 9781799877912 – Type: isbn-electronic Value: 9781799877929 Titles: – TitleFull: Advances in Malware and Data-Driven Network Security Type: main |
| ResultId | 1 |
