This repository contains a Python implementation of a Bloom filter. The project is designed to efficiently check whether an element is present in a set by using a probabilistic data structure. This implementation uses two files, rockyou.txt for loading initial values into the Bloom filter, and dictionary.txt for testing these values.
The Bloom filter is loaded with values from rockyou.txt. It then automates the testing of values in dictionary.txt, determining if each word is likely present in the rockyou.txt. The primary focus is on evaluating the effectiveness of the Bloom filter in distinguishing between words that are present or not in the dictionary.
- Load Data: Loads data from
rockyou.txtto populate the Bloom filter. - Automate Testing: Automatically tests all values from
dictionary.txt. - Statistics Calculation: Calculates and displays statistics on true positive, true negative, false positive, and false negative rates for the values tested against the
rockyou.txtdata set.
To run this Bloom filter implementation, simply execute the main.py script in your Python environment:
python main.pyThe software calculates and displays the following statistics to measure the accuracy of the Bloom filter:
True Positive (TP): Correctly identified as present in the Bloom filter. These are words that are in both rockyou.txt and dictionary.txt. True Negative (TN): Correctly identified as not present in the Bloom filter. These are words that are in neither file. False Positive (FP): Incorrectly identified as present in the Bloom filter. These are words that are in dictionary.txt but not in rockyou.txt. False Negative (FN): Incorrectly identified as not present in the Bloom filter. These are words that are in rockyou.txt but were not detected.
Please be aware that the rockyou.txt file is large and can impact the performance and memory usage of your system. The file can be download here
https://www.kaggle.com/datasets/wjburns/common-password-list-rockyoutxt