Enhanced Matrix Bloom Filter for Weak Password Identification

A single weak password exposes the entire network to an external threat. Password hacking is one of the most critical and commonly exploited for network security threats. A Bloom Filter (BF) is a simple space-efficient randomized data structure for representing a set in order to support membership queries. This compact representation is the payoff for allowing a small rate of false positives in membership queries; that is, queries might incorrectly recognize an element as member of the set. Matrix Bloom Filter (MBF) uses matrix representation of BFs to represent a data set. The false positive rate of MBF increases when the data set size increases. The proposed Enhanced Matrix Bloom Filter (EMBF) dynamically creates another bloom filter for the row which exceeds the given threshold value. This paper presents the identification of weak password using Enhanced Matrix Bloom Filter. It reduces the false positive rate if the word set size dynamically increases. The results of the experiment are examined on weak passwords and demonstrate the performance of EMBF and BF.