![]() I know nobody but us cares about entropy. Keeping in mind I'm after entropy only so to give users a cost-to-crack estimate. What metrics could be used to calculate human-created passwords so the result is much less secure looking than machine randoms? ![]() But I need to hear from the pros and looking for other ideas. I tried using log(pow(2500, 4))/log(2) => 4 words, 2500 possible combinations based on people using easier-to-remember words, as a percentage of the average human vocabulary of about 20,000 and this gave a resulting entropy of 45.15. Obviously, this would not be a good estimation. Human created non-random - isAwtheSUN = 57.37 bits entropy Issue with standard password entropy calc methods:ġPassword machine random - rmrgKDAyeY = 57.37 bits entropy The trouble is some human passwords seem stronger than machine crypto random: Now machine random has its own set of entropy calculation issues such as whether it is a totally random sequence, is it a symbol-separated word sequence chosen from a 307,111 word list, etc, etc. I will give users a switch to flip, whether the password is human-created or machine random. Here is a common problem which leads to my question, password entropy. I think this can communicate well to users in a way that is real to them. I want to calculate and communicate users' password entropy by cost to crack in the same way 1Password has here. I also want to give a ROUGH metric in addition to the strength tester. This one, which I've copied (with minor adjustments). I am including a fairly good password strength algorithm for my app for users on sign-up. Finally, this investigation determined that the use of password management software together with electronic and verbal secondary information user-awareness efforts resulted in an increase in password entropy.Okay, I know it might seem this has already been beaten to death but, hear me out. The research results further illustrated that the use of a password management application when coupled with verbal secondary information awareness efforts also resulted in a significant increase in average password entropy. The research results also indicated that the use of a password management application when coupled with electronic secondary information awareness efforts did result in a significant increase in average password entropy. ![]() The research results revealed that the use of a password management application resulted in an increase in average password entropy, but at a level that was not significant. This research also provided data to determine if efforts to increase user-awareness of password strength affected the selection of passwords. This study provided empirical data to determine if the entropy of user-selected passwords was affected by the use of password management software. Nonetheless, traditional alphanumeric-based passwords remain the most common form of user authentication and are expected to remain so for the foreseeable future. As a result of attacks targeting alphanumeric passwords, different authentication methods have been proposed. These attacks are based on the common perception that traditional alphanumeric passwords are weak and susceptible to attack. Specifically, attacks that target user authentication are increasingly popular. Attacks on information systems continue to rise. Maintaining the security of information contained within computer systems poses challenges for users and administrators.
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