Implementation of Improved Hash and Mapping Modified Low Power Parallel Bloom Filter Design

In this article, the authors present an investigation on bloom filters and introduce a new improved variant, which uses a secure modified hash function and suggested improved mapping scheme with an efficient parallel architecture. This novel architecture provides efficient, relatively fast membership querying and compact information representation with negligible false positive. This is relatively a low power and secure design with very less false positive ratio when compared with the traditional bloom filters. The design has been evaluated and tested using Xilinx 65 nm Virtex-5 field-programmable gate array as the target technology. The performance matrices are false positive ratio, power, speed and compactness.

[1]  Pramod K. Varshney,et al.  Data-aggregation techniques in sensor networks: a survey , 2006, IEEE Communications Surveys & Tutorials.

[2]  Yossi Matias,et al.  Spectral bloom filters , 2003, SIGMOD '03.

[3]  Jie Wu,et al.  The Dynamic Bloom Filters , 2010, IEEE Transactions on Knowledge and Data Engineering.

[4]  Abhishek Kumar,et al.  Space-code bloom filter for efficient traffic flow measurement , 2003, IMC '03.

[5]  Sotirios G. Ziavras,et al.  Efficient hardware support for pattern matching in network intrusion detection , 2010, Comput. Secur..

[6]  Jie Gao,et al.  Weighted Bloom filter , 2006, 2006 IEEE International Symposium on Information Theory.

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

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

[9]  Haoyu Song,et al.  Fast hash table lookup using extended bloom filter: an aid to network processing , 2005, SIGCOMM '05.

[10]  Ming Zhong,et al.  Optimizing data popularity conscious bloom filters , 2008, PODC '08.

[11]  Bernard Chazelle,et al.  The Bloomier filter: an efficient data structure for static support lookup tables , 2004, SODA '04.

[12]  Michael Mitzenmacher,et al.  Compressed bloom filters , 2002, TNET.

[13]  Taskin Koçak,et al.  Low-power bloom filter architecture for deep packet inspection , 2006, IEEE Communications Letters.

[14]  Taskin Koçak,et al.  Fully pipelined bloom filter architecture , 2008, IEEE Communications Letters.

[15]  Christian Esteve Rothenberg,et al.  The deletable Bloom filter: a new member of the Bloom family , 2010, IEEE Communications Letters.

[16]  Andrei Broder,et al.  Network Applications of Bloom Filters: A Survey , 2004, Internet Math..

[17]  Fang Hao,et al.  Building high accuracy bloom filters using partitioned hashing , 2007, SIGMETRICS '07.

[18]  David Wetherall,et al.  Forwarding without loops in Icarus , 2002, 2002 IEEE Open Architectures and Network Programming Proceedings. OPENARCH 2002 (Cat. No.02EX571).

[19]  Yuan He,et al.  Receiver-oriented design of Bloom filters for data-centric routing , 2010, Comput. Networks.

[20]  S. Srinivasa Rao,et al.  An optimal Bloom filter replacement , 2005, SODA '05.

[21]  David Hutchison,et al.  Scalable Bloom Filters , 2007, Inf. Process. Lett..

[22]  Alok N. Choudhary,et al.  An FPGA-Based Network Intrusion Detection Architecture , 2008, IEEE Transactions on Information Forensics and Security.

[23]  Hyesook Lim,et al.  Hierarchical packet classification using a Bloom filter and rule-priority tries , 2010, Comput. Commun..

[24]  Nikos Vrakas,et al.  Utilizing bloom filters for detecting flooding attacks against SIP based services , 2009, Comput. Secur..

[25]  John W. Lockwood,et al.  Fast and Scalable Pattern Matching for Network Intrusion Detection Systems , 2006, IEEE Journal on Selected Areas in Communications.

[26]  George Varghese,et al.  Beyond bloom filters: from approximate membership checks to approximate state machines , 2006, SIGCOMM 2006.

[27]  Li Fan,et al.  Summary cache: a scalable wide-area web cache sharing protocol , 2000, TNET.