Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/345769
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dc.coverage.spatialInformation Technology
dc.date.accessioned2021-10-28T08:44:54Z-
dc.date.available2021-10-28T08:44:54Z-
dc.identifier.urihttp://hdl.handle.net/10603/345769-
dc.description.abstractCloud computing provides a convenient technique to obtain services, resources and applications across the Internet. Research shows that different kind of DDoS attacks on cloud result in different effects. This thesis proposes a novel architecture that combines a well posed stacked sparse AutoEncoder (AE) for feature learning with a Deep Neural Network (DNN) for classification of network traffic into benign traffic and DDoS attack traffic. AE and DNN are optimized for detection of DDoS attacks by tuning the parameters using appropriately designed techniques. The improvements suggested in this thesis lead to low reconstruction error, prevent exploding and vanishing gradients, and lead to smaller network which avoids overfitting. A comparative analysis of the proposed approach with ten state-of-the-art approaches using performance metrics-detection accuracy, precision, recall and F1- Score, has been conducted. Experiments have been performed on CICIDS2017 and NSL-KDD standard datasets for validation. Proposed approach outperforms existing approaches over the NSLKDD dataset and yields competitive results over the CICIDS2017 dataset. After the detection of attacks, it is crucial to mitigate these attacks. So, this thesis also proposes a novel filtering-based approach for mitigation of DDoS attacks. This approach provides counters against DDoS attacks launched via IoT based botnets and zombies. The CAPTCHA based on gesture verification is used to filter out bots from humans. The proposed approach will track the behavior of clients accessing the network. The clients gain trust by accepting and replying correctly to the challenge. Only the trusted clients are given access to the network, otherwise they are blocked. The proposed gesture based CAPTCHA approach is more reliable than the other techniques like text based, image based, audio based, puzzle based etc. newline
dc.format.extentviii, 164p.
dc.languageEnglish
dc.relation-
dc.rightsuniversity
dc.titleDetection and mitigation of cloud based distributed denial of service attacks
dc.title.alternative
dc.creator.researcherBhardwaj, Aanshi
dc.subject.keywordAnomaly Detection
dc.subject.keywordCloud Computing
dc.subject.keywordDDoS Attacks
dc.subject.keywordDeep Learning
dc.description.noteBibliography 148-160p. Appendix 161-163p. Publication 164p.
dc.contributor.guideVig, Renu and Mangat, Veenu
dc.publisher.placeChandigarh
dc.publisher.universityPanjab University
dc.publisher.institutionUniversity Institute of Engineering and Technology
dc.date.registered2016
dc.date.completed2020
dc.date.awarded2020
dc.format.dimensions-
dc.format.accompanyingmaterialCD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:University Institute of Engineering and Technology

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01_title page.pdfAttached File90.22 kBAdobe PDFView/Open
02_correction certificate.pdf99.6 kBAdobe PDFView/Open
03_table of content.pdf86.98 kBAdobe PDFView/Open
04_acknowledgements.pdf182.28 kBAdobe PDFView/Open
05_list of figures.pdf541.85 kBAdobe PDFView/Open
06_list of tables.pdf368.89 kBAdobe PDFView/Open
07_abtract.pdf360.27 kBAdobe PDFView/Open
08_chapter 1.pdf875.9 kBAdobe PDFView/Open
09_chapter 2.pdf1.05 MBAdobe PDFView/Open
10_chapter 3.pdf1.49 MBAdobe PDFView/Open
11_chapter 4.pdf1.03 MBAdobe PDFView/Open
12_chapter 5.pdf1.19 MBAdobe PDFView/Open
13_chapter 6.pdf1.37 MBAdobe PDFView/Open
14_chpater 7.pdf768.49 kBAdobe PDFView/Open
15_chapter 8.pdf423.6 kBAdobe PDFView/Open
16_references.pdf788.82 kBAdobe PDFView/Open
17_list of acronyms.pdf481 kBAdobe PDFView/Open
18_publications.pdf606.61 kBAdobe PDFView/Open
80_recommendation.pdf423.6 kBAdobe PDFView/Open


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