Looking at the current hacking and fraud deals taking place, there is urgency in the need for a better solution to help create stronger passwords. Thus, the researchers have been trying their best to find out a technology to help people create and remember their password.
An U.S. Assistant Professor, Blase Ur, and his team from the University of Chicago along with Professor Nicolas Christin from the Carnegie Mellon University, Pennsylvania have disclosed a new up-to-date security code meter that will not only offer a real-time feedback but also give suggestions to the people regarding the created or to be created password. The meter technology helped people create stronger passwords, which weren’t difficult to recollect compared to the earlier non-feedback created passwords.
According to Blase, rather than just giving a strong or weak label to the passwords, a data-determined feedback is more beneficial in terms of security. The meter technology has basically been built to keep you out of the hackers attack. The meter works on a very huge artificial neural network that is a multifarious and large map resembling the neural network that runs in a human brain. The meter or the network usually has been designed to scan the millions of already existing passwords and identifying the specific traits or trends, if any. In short, the meter will let one know if there is any characteristic in your created password that the hackers can guess.
The researchers thought that it is better to let people know the reason why their password is bad or weak rather than just letting them know “Your security code is weak.” The meter technology has been created as a real-time data-driven feedback network owing to the password being typed letter by letter.
The attackers commonly exploit the normal pattern followed and guess the passwords by breaching into a large dataset. It is not easy to fool the attackers by changing the Es to 3S. Hence, this meter will help one know the prevalence of certain letters being used and instead provide the best option.
For now, the researchers plan to publicize their work at the “CHI 2017 conference” in Denver, Colorado before commercializing it.
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