Please use this identifier to cite or link to this item:
http://10.1.7.192:80/jspui/handle/123456789/9541
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Sheth, Kavisha | - |
dc.date.accessioned | 2021-01-05T06:12:46Z | - |
dc.date.available | 2021-01-05T06:12:46Z | - |
dc.date.issued | 2020-06-01 | - |
dc.identifier.uri | http://10.1.7.192:80/jspui/handle/123456789/9541 | - |
dc.description.abstract | In today's world and institution find themselves in complex situation for securing their system and data from increasing vulnerabilities that is why it would be better to discover and identify these vulnerabilities in advance before attacker can exploit them. Thus vulnerability assessment and penetration testing techniques helps it to determine whether the arrangements in securing system are working properly or not by fixing those security gaps. The report consists of two main sections, the first section dealing with existing techniques of vulnerability testing and second section on a new method which employs machine learning techniques for detecting malicious activity like Phishing URL,spam mail and malicious file . | en_US |
dc.publisher | Institute of Technology | en_US |
dc.relation.ispartofseries | 18MCEI12; | - |
dc.subject | Computer 2018 | en_US |
dc.subject | Project Report 2018 | en_US |
dc.subject | Computer Project Report | en_US |
dc.subject | Project Report | en_US |
dc.subject | 18MCEI | en_US |
dc.subject | 18MCEI12 | en_US |
dc.subject | INS | en_US |
dc.subject | INS 2018 | en_US |
dc.subject | CE (INS) | en_US |
dc.title | Penetration Testing using MachineLearning | en_US |
dc.type | Dissertation | en_US |
Appears in Collections: | Dissertation, CE (INS) |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
18MCEI12.pdf | 18MCEI12 | 2.53 MB | Adobe PDF | ![]() View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.