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Conference object . 2021
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Article . 2021
License: CC BY
Data sources: Datacite
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Article . 2021
License: CC BY
Data sources: Datacite
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Identifying Subdomains of the Website Using SUBLIST3R and Comparing SUBLIST3R AMASS, KNOCKPY

Authors: Arunima Santhosh; Rinimol Kurian;

Identifying Subdomains of the Website Using SUBLIST3R and Comparing SUBLIST3R AMASS, KNOCKPY

Abstract

Abstract : This research paper discusses a lots of subdomain enumeration tools. There are various number of subdomain enumeration tools in kali. In this paper we discuss about Sublist3r, Amass, and KnockPy. These tools are mainly used to enumerate subdomain of website using OSINT. It helps penetration testers and bug hunters collect and gather subdomains for the domain they are targeting. In general, system security has become a crucial element in the digital aspect. Technically, evaluating the system there are aspects, one of which is by conducting a security assessment. Specifically, each system that will be evaluated is the essence of vulnerability search. Reconnaissance technique is mainly used for gathering information about computer systems and the entities they belong to. To make use of any system, a hacker might use various tools and technologies. This information is very useful to a hacker who is trying to crack a whole system. Subdomain enumeration plays a vital role in reconnaissance. Enumeration of subdomains provide an important insight towards the various underlying architecture and enable to find hidden user interfaces and admin panels. The less infrequent and unknown the domain name, the less visitors will visit the site. This enables a blind spot for the easy finding of low hanging vulnerabilities. What i am going to do in this paper is to identify various subdomains of website using Sublist3r ,a subdomain enumeration kali tool. Some of the other most popular tools used for recon on domains are Amass, SubFinder and KnockPy. In this paper also include comparative study and analysis of various functions of these tools on parameters like uniqueness, accuracy, and conclude with work in certain scenarios along with static code analysis to find weak spots within the code infrastructure of each of the tool.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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