Intelligent message filter not updating

23-Apr-2016 16:23 by 4 Comments

Intelligent message filter not updating - Free Online

Luckily we can control the amount of false positives we receive with a trade off of time and memory.You may have never heard of a bloom filter before but you've probably interacted with one at some point.

When you visit a website it checks if that domain is in the filter.IBM Web Sphere Application Server provides periodic fixes for the base and Network Deployment editions of release V8.5.The following is a complete listing of fixes for V8.5 with the most recent fix at the top.Bloom filters are super efficient data structures that allow us to tell if an object is most likely in a data set or not by checking a few bits.Bloom filters return some false positives but no false negatives.For each time an object is hashed the corresponding hash value in the bit array is then marked as 1. Rather than needing say 4 bytes to store a 1 or a 0 we can simply do it in a bit.

Here's an example to help make it easier to understand.

Note we mod by the size of the bit array to prevent index out of bounds: from bitarray import bitarray import mmh3 bit_array = bitarray(10) bit_array.setall(0) b1 = mmh3.hash("hello", 41) % 10 #Equals 0 bit_array[b1] = 1 b2 = mmh3.hash("hello", 42) % 10 #Equals 4 bit_array[b2] = 1 The reason it is only probably in the set is because a combination of items added to the data set could end up setting the same bits to 1.

This prevents you from having to ping Google's servers every time you visit a website to check if it's malicious or not.

Large databases such as Cassandra and Hadoop use bloom filters to see if it should do a large query or not.

From Cassandra's Architecture Overview: Cassandra uses bloom filters to save IO when performing a key lookup: each [Sorted String Table] has a bloom filter associated with it that Cassandra checks before doing any disk seeks, making queries for keys that don't exist almost free. Bloom filters work by hashing an object several times using either multiple hash functions or the same hash function with a different seed.

This insures that when we hash an object we're unlikely to get the same result.