A Multibit Representation of Bloom Filter for Simultaneous Acquisition of Membership and Attribute Information

As dataflow on Internet is growing exponentially, processes that can efficiently extract meaningful information have become a crucial factor for many successful applications. For example, the purpose of membership determination is to discriminate whether a fragment of the dataflow is an element of a specific dataset. The Bloom filter has been well recognized for dealing with such a problem, but it can only provide the membership information. Recently, membership determination that can accompany with additional attribute information has becoming increasingly important, because it could save considerable time for the secondary querying once the membership is confirmed. Therefore, this study proposes a multibit representation of the original Bloom filter by encoding the attribute codes, instead of binary counterpart, for resolving such a situation. Simulation results show that the querying efficiency and false-positive ratios are fairly competitive with reasonable memory-space usages.

[1]  Yu Hua,et al.  Using Parallel Bloom Filters for Multiattribute Representation on Network Services , 2010, IEEE Transactions on Parallel and Distributed Systems.

[2]  Mohd Juzaiddin Ab Aziz,et al.  Anomalies Classification Approach for Network-based Intrusion Detection System , 2016, Int. J. Netw. Secur..

[3]  Kenneth J. Christensen,et al.  A new analysis of the false positive rate of a Bloom filter , 2010, Inf. Process. Lett..

[4]  D. Ellison,et al.  On the Convergence of the Multidimensional Albus Perceptron , 1991, Int. J. Robotics Res..

[5]  Shigang Chen,et al.  When Bloom Filters Are No Longer Compact: Multi-Set Membership Lookup for Network Applications , 2016, IEEE/ACM Transactions on Networking.

[6]  Tong Yang,et al.  A Shifting Framework for Set Queries , 2017, IEEE/ACM Transactions on Networking.

[7]  Dan Feng,et al.  Locality-Sensitive Bloom Filter for Approximate Membership Query , 2012, IEEE Transactions on Computers.

[8]  Peyman Kabiri,et al.  Feature Selection for Intrusion Detection System Using Ant Colony Optimization , 2016, Int. J. Netw. Secur..

[9]  Hao Wang,et al.  ID Bloom Filter: Achieving Faster Multi-Set Membership Query in Network Applications , 2018, 2018 IEEE International Conference on Communications (ICC).

[10]  Burton H. Bloom,et al.  Space/time trade-offs in hash coding with allowable errors , 1970, CACM.

[11]  Wei Wang,et al.  Noisy Bloom Filters for Multi-Set Membership Testing , 2016, SIGMETRICS.

[12]  R. Ganesan,et al.  Cryptographically imposed model for Efficient Multiple Keyword-based Search over Encrypted Data in Cloud by Secure Index using Bloom Filter and False Random Bit Generator , 2017, Int. J. Netw. Secur..

[13]  Heng Ma,et al.  A CMAC-based scheme for determining membership with classification of text strings , 2015, Neural Computing and Applications.

[14]  Min-Shiang Hwang,et al.  A Survey of Attribute-based Access Control with User Revocation in Cloud Data Storage , 2016, Int. J. Netw. Secur..

[15]  Michael T. Goodrich,et al.  Invertible bloom lookup tables , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).