Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/522109
Title: | Efficient network attack detection based on deep learning techniques |
Researcher: | Poornima,R |
Guide(s): | Singaravel,G |
Keywords: | Deep Learning Information And Communication Engineering Machine Learning |
University: | Anna University |
Completed Date: | 2023 |
Abstract: | The significant growth in the use of the Internet and the rapid newlinedevelopment of network technologies are associated with an increased risk of newlinenetwork attacks. Network attacks refer to all types of unauthorized access to a newlinenetwork including any attempts to damage and disrupt the network, often newlineleading to serious consequences. The Internet helps foster connection and newlinecommunication, but the integrity and confidentiality of these connections and newlineinformation exchanges can be violated and compromised by attackers who newlineseek to damage and disrupt network connections and network security. newlineEvolution of different types of network attacks and an increase in the data newlineexchange between the computing devices pose the requirement to secure newlinenetwork and computing devices. newline newline |
Pagination: | xxii,195p. |
URI: | http://hdl.handle.net/10603/522109 |
Appears in Departments: | Faculty of Information and Communication Engineering |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
01_title.pdf | Attached File | 46.8 kB | Adobe PDF | View/Open |
02_prelim_pages.pdf | 1.2 MB | Adobe PDF | View/Open | |
03_content.pdf | 219.04 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 217.42 kB | Adobe PDF | View/Open | |
05_chapter 1.pdf | 1.06 MB | Adobe PDF | View/Open | |
06_chapter 2.pdf | 436.53 kB | Adobe PDF | View/Open | |
07_chapter 3.pdf | 1.11 MB | Adobe PDF | View/Open | |
08_chapter 4.pdf | 857.13 kB | Adobe PDF | View/Open | |
09_chapter 5.pdf | 1.16 MB | Adobe PDF | View/Open | |
10_chapter 6.pdf | 899.11 kB | Adobe PDF | View/Open | |
11_annexures.pdf | 421.24 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 178.88 kB | Adobe PDF | View/Open |
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