Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/571115
Title: A deep learning approach for detection of DDoS attacks
Researcher: Meenakshi
Guide(s): Krishan Kumar and Behal, Sunny
Keywords: Auto encoder
CICDDoS2019
DDoS AT 2022
DDoS Attacks
Deep Neural Network
Gated Recurrent Unit
Long Short Term Memory
Network Security
University: Panjab University
Completed Date: 2023
Abstract: Despite its numerous benefits, the Internet is prone to various crimes such as misinformation dissemination, hacking, and attacks. A Denial of Service (DoS) attack is a type of attack that prevents access to online services. If the attack is carried out using one machine, it is referred to as a DoS attack. To mitigate these consequences, it is essential to have a method of detecting DDoS attacks. Although there are many security solutions available, the attackers frequent changes in attack methods pose a challenge for security systems to remain up-to-date. Additionally, existing Machine Learning (ML) strategies are limited to known attack patterns and require annotated data. The existing prominent research in network security has heavily relied on publicly available simulated datasets to test defense mechanisms. Further, the significant increase in network traffic volume over time has caused most existing DDoS defense solutions to fail, as they have not been validated on large volumes of traffic. Additionally, many existing datasets have been created through simulation, and those generated through emulation do not adequately represent a diverse range of attack types. The thesis s 1st contribution involves performing a systematic review of the literature on DL-based detection systems. The objective is to categorize state-of-the-art Deep Learning (DL) methods for identifying Distributed Denial of Service (DDoS) attacks according to common criteria. The 2nd contribution of this thesis involves examining and comparing datasets from 2011 to 2021. This analysis is useful for presenting taxonomy of DDoS attacks and developing a Testbed (TB) for the DDoS attacks at the Application and Transport layer 2022 (DDoSAT-2022) dataset. The 3rd contribution involves developing a method to detect DDoS attacks using DL algorithms and features from two datasets - CICDDoS2019 and DDoS-AT-2022. newline
Pagination: xix,171p.
URI: http://hdl.handle.net/10603/571115
Appears in Departments:University Institute of Engineering and Technology

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01_title.pdfAttached File30.56 kBAdobe PDFView/Open
02_prelim pages.pdf360.9 kBAdobe PDFView/Open
03_chapter 1.pdf422.18 kBAdobe PDFView/Open
04_chapter 2.pdf5.75 MBAdobe PDFView/Open
05_chapter 3.pdf277.88 kBAdobe PDFView/Open
06_chapter 4.pdf1.51 MBAdobe PDFView/Open
07_chapter 5.pdf1.32 MBAdobe PDFView/Open
08_chapter 6.pdf738.25 kBAdobe PDFView/Open
09_chapter 7.pdf63.18 kBAdobe PDFView/Open
10_annexures.pdf150.63 kBAdobe PDFView/Open
80_recommendation.pdf93.16 kBAdobe PDFView/Open
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