Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/499745
Title: A Framework for Attack Detection in Iot Using Optimal Feature Selection Approach and Random Forest Algorithm
Researcher: Sandhya, E
Guide(s): Annapurani Panaiyappan, K
Keywords: Computer Science
Computer Science Information Systems
Engineering and Technology
University: SRM Institute of Science and Technology
Completed Date: 2023
Abstract: The Internet of Things (IoT) combines software and hardware for data collection and sharing, including networks, sensors and electronics. To interchange data within the network, physical devices with distinct IP addresses connect with outside parties through the internet. The IoT concept has gained importance in the development of smart environments, including smart cities and households, which encompass a variety of application fields. By addressing issues with the living environment, industrial needs, and energy consumption, such smart settings aim to make daily activities in human life more creative and competitive. newlineThe major goal is clearly evident due to the dramatic increase of IoT services and applications across different networks. Furthermore, IoT systems are exposed to a variety of security vulnerabilities, including flooding and probing attacks. These types of attacks unquestionably cause serious harm to IoT devices and the applications that enable heterogeneous devices in an IoT ecosystem. These system entities are open to severe attacks due to a lack of security measures newline
Pagination: 
URI: http://hdl.handle.net/10603/499745
Appears in Departments:Department of Computer Science Engineering

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02_preliminary page.pdf.pdf361.94 kBAdobe PDFView/Open
03_content.pdf231.64 kBAdobe PDFView/Open
04_abstract.pdf204.89 kBAdobe PDFView/Open
05_chapter 1.pdf1.05 MBAdobe PDFView/Open
06_chapter 2.pdf737.73 kBAdobe PDFView/Open
07_chapter 3.pdf1.08 MBAdobe PDFView/Open
08_chapter 4.pdf1.06 MBAdobe PDFView/Open
09_chapter 5.pdf217.11 kBAdobe PDFView/Open
10_annexures.pdf263.1 kBAdobe PDFView/Open
80_recommendation.pdf254.91 kBAdobe PDFView/Open
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