| Autor: |
Anitha, C., Nalina, E., Sivaprakash, T., Indumathi, G. |
| Předmět: |
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| Zdroj: |
AIP Conference Proceedings; 2024, Vol. 2802 Issue 1, p1-9, 9p |
| Abstrakt: |
The Internet has become an inevitable part of humans living in 21 st century. It has eased our lives in such a way that one could do almost anything from paying bills to placing orders or to sneak onto social media or to take part in an e-auction sitting relaxed on the living room's couch. As the saying goes on "Too much of anything is good for Nothing", The extravagant usage of the internet cast light on its lurking darker side posing serious perils to the users. The technology grows exponentially paving a way to collapse oneself bereaved of money even without his knowledge. Identity theft and fraudsters are two major traps that one has to be cautioned of in this transcendental World Wide Web. Malevolent web links are the malicious channels that may host unsolicited elements like a virus, malware, spyware, phishing traps. Recent Pegasus has the potential to tap the users that don't require even a click or download from the user's end. The user's complete Message history, call history, App usages, and the most sensitive financial transactions are revealed to the perpetrator. In this paper, we study various machine learning approaches towards the detection of malevolent URLs that have been proved to be effective when compared to the non-machine learning approaches that have been existing conventionally. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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