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DC Field | Value | Language |
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dc.contributor.author | Vidhu Kiran | - |
dc.contributor.author | Supervisor: Shaveta Rani | - |
dc.date.accessioned | 2023-02-06T10:34:26Z | - |
dc.date.available | 2023-02-06T10:34:26Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/391 | - |
dc.description.abstract | In recent years, the popularity of the Internet of Things (IoT) has risen as an emerging technology with its applications in all parts of the daily lives. However, IoT faces inherent resource and computing constraints, which introduces several challenges in the network. One of the major concerns is security, where security attacks exploit the vulnerabilities of it to perform malicious activities in the network. Even though many research works focus on providing security in the RPL network, the resource constraints in terms of data storage and power that reduces their potential in providing adequate security mechanisms. In addition to the traditional security issues, the need to confirm the trust ability and cooperativeness of all nodes is also a crucial requirement for properly maintaining the network functionality. The proposed contributions provide effective detection mechanisms for detecting severe security attacks such as Distributed Denial of Service (DDoS) attacks, selective forwarding attacks, and malicious dropping attacks to solve issues. The first contribution designs the trust-based detection mechanisms against DDoS attacks such as Trust based DDOS Attack Detection (TDD) mechanism and Subjective Logic-based Trust Mechanism against DDoS (SLTD) based detection methodology. In TDD mechanism, the neighbor nodes perform the initial trust calculation based on the incoming packet count, and gateway nodes provide data frequency-based DDoS attacker detection. The implementation results prove that the proposed TDD mechanism provides improved performance in terms of detection accuracy, overhead, throughput and power consumption in comparison with the existing packet frequency-based attack detection. The SLTD methodology implements subjective logic-based attack detection with the initial trust calculation based on the incoming packet count. The performance comparison between the proposed SLTD methodology and intrusion detection mechanism without subjective logic is performed and the SLTD mechanism provides better performance in terms of detection accuracy, power consumption, overhead, and throughput. The second contribution implements trust-based detection mechanisms against selective forwarding attack, namely Trust-Based Selective Forwarding Attack Detection in RPL (TSFRPL) and Multi-Level Trust-Based Secure RPL over IoT (MLT-IoT). | en_US |
dc.language.iso | en | en_US |
dc.publisher | MRSPTU, Bathinda | en_US |
dc.subject | Internet of Things | en_US |
dc.title | Trust Based Secured Routing Mechanisms in Internet of Things | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | Ph.D Thesis |
Files in This Item:
File | Description | Size | Format | |
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01_ title.pdf | Title | 48.66 kB | Adobe PDF | View/Open |
02_prelim pages.pdf | Preliminary Pages | 229.5 kB | Adobe PDF | View/Open |
03_contents.pdf | Contents | 27.84 kB | Adobe PDF | View/Open |
05_chapter 1.pdf | Chapter 1 | 559.32 kB | Adobe PDF | View/Open |
06_chapter 2.pdf | Chapter 2 | 440.12 kB | Adobe PDF | View/Open |
07_chapter 3.pdf | Chapter 3 | 1.07 MB | Adobe PDF | View/Open |
08_chapter 4.pdf | Chapter 4 | 1.15 MB | Adobe PDF | View/Open |
09_chapter 5.pdf | Chapter 5 | 1.1 MB | Adobe PDF | View/Open |
10_chapter 6.pdf | Chapter 6 | 431.12 kB | Adobe PDF | View/Open |
11_annexures.pdf | Annexures | 443.06 kB | Adobe PDF | View/Open |
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