Nayeem, Irfan (2022) Mal-adware detection for URL security. Masters thesis, Dublin, National College of Ireland.
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Abstract
As Internet technology advances, network security is more and more exposed to various threats. In particular, malicious uniform resource locators (URLs) can be spread by attackers to carry out operations like spamming and phishing. Making progress in identifying malicious URLs is crucial for putting an end to these attacks. However, there are still several severe problems with the current findings. For instance, it could be challenging to identify problems precisely. Some of the detection methods used today are easy for attackers to circumvent. The vector input dimension affects the dynamic convolution technology's middle layer feature mapping width. The pooling layer settings are additionally continuously changed according to the depth of the present convolution layer and the duration of the URL input to obtain more precise features over a wider range. To acquire the embeddings of a URL, the innovative embedding approach bases word embedding on character anchoring.
Item Type: | Thesis (Masters) |
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Supervisors: | Name Email Khan, Imran UNSPECIFIED |
Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet |
Divisions: | School of Computing > Master of Science in Cyber Security |
Depositing User: | Tamara Malone |
Date Deposited: | 04 May 2023 14:26 |
Last Modified: | 04 May 2023 14:26 |
URI: | https://norma.ncirl.ie/id/eprint/6530 |
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