Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/340166
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dc.coverage.spatial
dc.date.accessioned2021-09-14T03:54:44Z-
dc.date.available2021-09-14T03:54:44Z-
dc.identifier.urihttp://hdl.handle.net/10603/340166-
dc.description.abstractWith an exponential increase in the data size and complexity of various seized items to be investigated, existing methods of network and computer forensics are not very efficient when it comes to dealing with accuracy and detection ratio. Till the time a well-established forensic technique is developed to handle security threats, a much more sophisticated attacks strike on network. Traditional Intrusion Detection Systems (IDS) and forensics techniques used to detect and prevent malicious network behaviours, fail to handle new or zero day attacks. The accuracy of Intrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS) is questionable, which can t be trusted for forensics. Another important drawback with the exiting techniques, is their inability to tackle high velocity and huge amount of heterogeneous data. Cyber forensic investigation mechanism has volume constraint, while processing the fast growing data from Information and Communication Technology (ICT) infrastructure, including IoT based devices and platforms. Non-tangible sources often don t have the limit of flowing data through them, especially through communication media. Hence, increasing the desperate requirement for an efficient benchmarking of big data analysis. Existing techniques exhibit inherent limitations in processing huge volume, variety, and velocity of data. It makes the process time-consuming and resource intensive. Available solutions to date have used an anomaly-based approach or have proposed approaches based on the deviation from a regular pattern. To tackle the seized bytes, authors have proposed an approach for big data forensics, with efficient sensitivity and precision. In order to maintain a balance between processing time and output efficiency, existing techniques put a limit on the amount of data under analysis, which results in a non-polynomial time complexity of these solutions.
dc.format.extent153p.
dc.languageEnglish
dc.relation
dc.rightsuniversity
dc.titleDesign and Implementation of Computer and Network Forensics Framework
dc.title.alternative
dc.creator.researcherChhabra, Gurpal Singh
dc.subject.keywordBig Data Forensics
dc.subject.keywordCyber Forensics
dc.subject.keywordNetwork Forensics
dc.description.note
dc.contributor.guideSingh, Maninder and Singh, Varinder Pal
dc.publisher.placePatiala
dc.publisher.universityThapar Institute of Engineering and Technology
dc.publisher.institutionDepartment of Computer Science and Engineering
dc.date.registered
dc.date.completed2019
dc.date.awarded
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science and Engineering

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01_title.pdfAttached File268.74 kBAdobe PDFView/Open
02_dedication.pdf163.19 kBAdobe PDFView/Open
03_certificate.pdf268.61 kBAdobe PDFView/Open
04_acknowlwedgement.pdf300.36 kBAdobe PDFView/Open
05_abstract.pdf177.06 kBAdobe PDFView/Open
06_table of contents.pdf201.49 kBAdobe PDFView/Open
07_list of figures.pdf191.26 kBAdobe PDFView/Open
08_list of tables.pdf175.65 kBAdobe PDFView/Open
09_chapter 1.pdf817.71 kBAdobe PDFView/Open
10_chapter 2.pdf1.13 MBAdobe PDFView/Open
11_chapter 3.pdf353.76 kBAdobe PDFView/Open
12_chapter 4.pdf608.99 kBAdobe PDFView/Open
13_chapter 5.pdf2.58 MBAdobe PDFView/Open
14_chapter 6.pdf1.21 MBAdobe PDFView/Open
15_chapter 7.pdf242.74 kBAdobe PDFView/Open
16_references.pdf421.64 kBAdobe PDFView/Open
17_research publications.pdf222.65 kBAdobe PDFView/Open
80_recommendation.pdf343.31 kBAdobe PDFView/Open


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