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Bhadan Janarthanan, Kousic (2021) Preventing the Inbound Malware Using Recommendation Algorithm. Masters thesis, Dublin, National College of Ireland.
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Abstract
Inbound Malware refers to threats that occur as a result of inbound traffic entering your network. Although an anti-virus system can help to safeguard, it cannot always prevent the appearance of an unfamiliar signature. This is because numerous types of malware are specifically developed to infiltrate the insider network server. Due to the high number of traffic data, the difficulties encountered in screening these inbound bad actors are frequently hard to evaluate. Since incoming connections can originate from a range of origins, the best approach to guarantee that inbound limitations are not allowing access to an inside system or protecting inbound security rules from "known-bad" behavior or activity is to utilize a combination of the two. Thus, utilizing the similarity technique with the recommendation system, the behavior activity data can be tracked, which aids in keeping bad actors out.
The model was implemented with the help of a support vector machine and the k-nearest neighbours approach. Then, using a recommendation mechanism prioritizes the level of danger. The attribute-based recommendation system operates here. As a result, these inbound traffic analyses will be used to prevent Malware.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | Intrusion Prevention System; Inbound Malware; Support Vector Algorithm; KNN Algorithm |
| 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 T Technology > T Technology (General) > Information Technology > Computer software |
| Divisions: | School of Computing > Master of Science in Cyber Security |
| Depositing User: | Clara Chan |
| Date Deposited: | 24 Nov 2022 19:07 |
| Last Modified: | 24 Nov 2022 19:07 |
| URI: | https://norma.ncirl.ie/id/eprint/5933 |
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