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Different systems store password hashes in different ways depending on the encryption used. Instead they store hashes of passwords and when authentication takes place, the password is hashes and if the hashes match authentication is successful. Most systems don’t store passwords on them. CaRP is not a panacea, but it offers reasonable security and usability and appears to fit well with some practical applications for improving online security.Want to get started with password cracking and not sure where to begin? In this post we’ll explore how to get started with it. CaRP also offers a novel approach to address the well-known image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. Notably, a CaRP password can be found only probabilistically by automatic online guessing attacks even if the password is in the search set. CaRP addresses a number of security problems altogether, such as online guessing attacks, relay attacks, and, if combined with dual-view technologies, shoulder-surfing attacks. CaRP is both a Captcha and a graphical password scheme. In this paper, we present a new security primitive based on hard AI problems, namely, a novel family of graph-ical password systems built on top of Captcha technology, which we call Captcha as graphical passwords (CaRP).

Using hard AI problems for security is emerging as an exciting new paradigm, but has been under-explored. Many security primitives are based on hard mathematical problems. The research suggests there is wide use of automated password tools and wordlists in attempts to gain access to the SSH honeypots, there are also a wide range of account types being probed. Automated password guessing using wordlists is one technique employed by cyber criminals in attempts to gain access to devices on the Internet.

Although all three honeypots are have the same configuration settings and are located on the same IPv4 /24 subnet work space, there is a variation between the numbers of activities recorded on each honeypots. The analysis in this paper focuses on the techniques used to attempt to gain access to these systems by attacking entities. The data from the honeypots were collected during the period 17th July 2012 and 26th November 2013, a total of 497 active day periods.

The honeypots used the same base software and hardware configurations. The honeypots were located on the same /24 IPv4 network and configured as identically as possible. This paper is an investigation focusing on activities detected by three SSH honeypots that utilise Kippo honeypot software.

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