Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/356189
Title: Effective Attack Handling Methods for Layer Wise Spoofing Based Man in the Middle Attacks in Machine to Machine Networks
Researcher: Sabitha Banu A
Guide(s): Padmavathi G
Keywords: Engineering and Technology
Computer Science
Computer Science Interdisciplinary Applications
University: Avinashilingam Institute for Home Science and Higher Education for Women
Completed Date: 2021
Abstract: Machine to Machine networks (M2M) is a technology where all the devices worldwide are newlineinterconnected. Because a growing number of people and companies rely on M2M networks, they are newlinevulnerable to Configuration attack, DoS attack, Man in the Middle attack (MITM) and User data, and newlineidentity privacy attacks. MITM attack is one of the most common types of security attacks and a newlinesignificant threat against security. The main motive of MITM attacks is to intercept the newlinecommunication to steal the credentials or modify the data without the victims knowledge. One of newlinemany MITM attacks Spoofing-based MITM attacks launched on the communication channel category newlinehave been taken for the research because Spoofing-based MITM attacks are predominant in M2M newlinenetworks. newlineThe four different types of Spoofing-based MITM attacks in each layer such as ARP Spoofingbased newlineMITM attack in Data Link layer, IP Spoofing-based MITM attack in Network layer, BGP newlineSpoofing-based MITM attack in Application layer, and DNS Spoofing-based MITM attack in newlineApplication layer. The main aim of this research is to provide security against Spoofing based MITM newlineattacks effectively. newlineExamining each of the Spoofing-based MITM attacks includes a detailed description of these newlinestages leading up to its discovery, building a hybrid deep learning detecting model, and evaluating newlineperformance. newlineHybrid deep learning methods namely ConvLSTM-ECC, BATELM-ECC, OBA-ECC-RSA, newlineFFOBLA-ECC are proposed to handle layer-wise Spoofing-based MITM attacks. newlineThe first phase of the thesis discusses the Proposed Hybrid ConvLSTM-ECC method that newlinedetects the Data Link layer s ARP Spoofing-based MITM attack nodes in a wired and wireless newlinescenario using convolutional layers for feature extraction from the raw data. The result is fed into the newlineLSTM model to predict the detection accuracy and it mitigates the ARP Spoofing-based MITM newlineattacks by generating signatures with the data for the node authentication. newlineThe second phase of the thesis presents the Proposed Hybrid BATELM-ECC method that newlinedetects the Network
Pagination: 183
URI: http://hdl.handle.net/10603/356189
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File3.48 kBAdobe PDFView/Open
02_certificate.pdf400.82 kBAdobe PDFView/Open
03_acknowledgement.pdf9.93 kBAdobe PDFView/Open
04_contents.pdf18.16 kBAdobe PDFView/Open
05_list of tables, figures and acronyms.pdf27.43 kBAdobe PDFView/Open
06_chapter 1.pdf511.72 kBAdobe PDFView/Open
07_chapter 2.pdf473.5 kBAdobe PDFView/Open
08_chapter 3.pdf286.64 kBAdobe PDFView/Open
09_chapter 4.pdf523.69 kBAdobe PDFView/Open
10_chapter 5.pdf678.43 kBAdobe PDFView/Open
11_chapter 6.pdf482.69 kBAdobe PDFView/Open
12_chapter 7.pdf595.64 kBAdobe PDFView/Open
13_chapter 8.pdf129.74 kBAdobe PDFView/Open
14_references.pdf323.65 kBAdobe PDFView/Open
15_annexures.pdf731.78 kBAdobe PDFView/Open
80_recommendation.pdf6.37 kBAdobe PDFView/Open
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