DETECTION OF MALWARES IN PDF FILE USING DEEP LEARNING.
In recent times, malware has been growing continuously as in our digital world. The detection of malware is important for recognizing cyber security problems in society. In recent years AI techniques are exploited to sense the malware. Based on the approach of signature for detecting the products li...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 689 - 697 |
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| Autores principales: | , |
| Formato: | pictorial tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151006022&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006022 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006022 151006022 151006022 151006022 ppf: 689 ppct: 8 formats: fmt: @attributes: type: P tig: atl: DETECTION OF MALWARES IN PDF FILE USING DEEP LEARNING. aug: au: RAO, GANGA RAMA KOTESWARA SAGAR, P. VIDYA affil: Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram sug: subj: Deep Learning Utilization Computer Viruses Electronic Publications Data Security Artificial Intelligence Neural Networks (Computer) Downloading (Database) Algorithms ab: In recent times, malware has been growing continuously as in our digital world. The detection of malware is important for recognizing cyber security problems in society. In recent years AI techniques are exploited to sense the malware. Based on the approach of signature for detecting the products like malware and antivirus with the help of assumption rules in order to identify and categorize the various groups in malware detection types. Due to the particular specified rules, it seems very difficult to acknowledge the newly occurred malware. In recent times, the widely used technique with improved performance even in larger datasets is Deep learning algorithm. Deep learning techniques are capable o to discriminate benevolent and malevolent files without cost and unreliable feature engineering. pubtype: Academic Journal doctype: pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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