The Experts below are selected from a list of 27825 Experts worldwide ranked by ideXlab platform

John Lockwood - One of the best experts on this subject based on the ideXlab platform.

  • Fast and scalable pattern matching for Content Filtering
    2005 Symposium on Architectures for Networking and Communications Systems (ANCS), 2005
    Co-Authors: Sarang Dharmapurikar, John Lockwood
    Abstract:

    High-speed packet Content inspection and Filtering devices rely on a fast multi-pattern matching algorithm which is used to detect predefined keywords or signatures in the packets. Multi-pattern matching is known to require intensive memory accesses and is often a performance bottleneck. Hence specialized hardware-accelerated algorithms are being developed for line-speed packet processing. While several pattern matching algorithms have already been developed for such applications, we find that most of them suffer from scalability issues. To support a large number of patterns, the throughput is compromised or vice versa. We present a hardware-implementable pattern matching algorithm for Content Filtering applications, which is scalable in terms of speed, the number of patterns and the pattern length. We modify the classic Aho-Corasick algorithm to consider multiple characters at a time for higher throughput. Furthermore, we suppress a large fraction of memory accesses by using Bloom filters implemented with a small amount of on-chip memory. The resulting algorithm can support matching of several thousands of patterns at more than 10 Gbps with the help of a less than 50 KBytes of embedded memory and a few megabytes of external SRAM. We demonstrate the merit of our algorithm through theoretical analysis and simulations performed on Snort's string set.

Liu Zhi-jing - One of the best experts on this subject based on the ideXlab platform.

  • Personalized text Content Filtering based on typical feedbacks
    Journal of Computer Applications, 2007
    Co-Authors: Liu Zhi-jing
    Abstract:

    This paper presented an approach to perform text Content Filtering based on Content-based and collaborative Filtering,using the Probability Model.Introducing the idea of Stereotypic inference,it classified the users into different types and built the model for each type.Moreover,the refreshing of the profiles was based on the feedbacks of the model,and the building of the new profiles was based on the typical profiles.In this way,the precision and the recall were improved significantly.

Sarang Dharmapurikar - One of the best experts on this subject based on the ideXlab platform.

  • Fast and scalable pattern matching for Content Filtering
    2005 Symposium on Architectures for Networking and Communications Systems (ANCS), 2005
    Co-Authors: Sarang Dharmapurikar, John Lockwood
    Abstract:

    High-speed packet Content inspection and Filtering devices rely on a fast multi-pattern matching algorithm which is used to detect predefined keywords or signatures in the packets. Multi-pattern matching is known to require intensive memory accesses and is often a performance bottleneck. Hence specialized hardware-accelerated algorithms are being developed for line-speed packet processing. While several pattern matching algorithms have already been developed for such applications, we find that most of them suffer from scalability issues. To support a large number of patterns, the throughput is compromised or vice versa. We present a hardware-implementable pattern matching algorithm for Content Filtering applications, which is scalable in terms of speed, the number of patterns and the pattern length. We modify the classic Aho-Corasick algorithm to consider multiple characters at a time for higher throughput. Furthermore, we suppress a large fraction of memory accesses by using Bloom filters implemented with a small amount of on-chip memory. The resulting algorithm can support matching of several thousands of patterns at more than 10 Gbps with the help of a less than 50 KBytes of embedded memory and a few megabytes of external SRAM. We demonstrate the merit of our algorithm through theoretical analysis and simulations performed on Snort's string set.

  • ANCS - Fast and scalable pattern matching for Content Filtering
    Proceedings of the 2005 symposium on Architecture for networking and communications systems - ANCS '05, 2005
    Co-Authors: Sarang Dharmapurikar, John W. Lockwood
    Abstract:

    High-speed packet Content inspection and Filtering devices rely on a fast multi-pattern matching algorithm which is used to detect predefined keywords or signatures in the packets. Multi-pattern matching is known to require intensive memory accesses and is often a performance bottleneck. Hence specialized hardware-accelerated algorithms are being developed for line-speed packet processing. While several pattern matching algorithms have already been developed for such applications, we find that most of them suffer from scalability issues. To support a large number of patterns, the throughput is compromised or vice versa. We present a hardware-implementable pattern matching algorithm for Content Filtering applications, which is scalable in terms of speed, the number of patterns and the pattern length. We modify the classic Aho-Corasick algorithm to consider multiple characters at a time for higher throughput. Furthermore, we suppress a large fraction of memory accesses by using Bloom filters implemented with a small amount of on-chip memory. The resulting algorithm can support matching of several thousands of patterns at more than 10 Gbps with the help of a less than 50 KBytes of embedded memory and a few megabytes of external SRAM. We demonstrate the merit of our algorithm through theoretical analysis and simulations performed on Snort's string set.

Kai Zheng - One of the best experts on this subject based on the ideXlab platform.

  • a distributed tcam coprocessor architecture for integrated longest prefix matching policy Filtering and Content Filtering
    IEEE Transactions on Computers, 2013
    Co-Authors: Zhiping Cai, Zhijun Wang, Kai Zheng, Jiannong Cao
    Abstract:

    Longest Prefix Matching (LPM), Policy Filtering (PF), and Content Filtering (CF) are three important tasks for Internet nowadays. It is both technologically and economically important to develop integrated solutions to the effective execution of the three tasks. To this end, in this paper, we propose a distributed Ternary Content Addressable Memory (TCAM) coprocessor architecture. The integrated solution exploits the complementary lookup load and storage load requirements of the three tasks to balance the lookup load and storage load among the TCAMs. A prefix Filtering-based CF algorithm is designed to reduce the lookup load and a novel cache system is developed to dynamically handle the lookups from overloaded TCAMs. Simulations based on real-world traffic traces show that the proposed solution can perform all three tasks given a 10 Gbps line rate using only the resources required to perform just the CF task given a 10 Gbps line rate.

  • ICC - A Distributed TCAM Coprocessor Architecture for Integrated Policy Filtering and Content Filtering
    2010 IEEE International Conference on Communications, 2010
    Co-Authors: Zhiping Cai, Zhijun Wang, Kai Zheng
    Abstract:

    Policy Filtering (PF) and Content Filtering (CF) are two important tasks in packet forwarding of today's Internet. It is both technologically and economically important to develop integrated solutions for executing both tasks to reduce cost. In this paper, we propose a distributed Ternary Content Addressable Memory (TCAM) coprocessor architecture to allow fast and integrated PF and CF. Due to significant requirement diversities in both lookup load and storage load between PF and CF, the integrated solution exploits the complementary characteristics of the two tasks and well balances both the lookup load and storage load among TCAMs. A prefix Filtering based CF algorithm is designed to reduce the lookup load and a novel cache mechanism is developed to dynamically handle the lookups from overloaded TCAMs. Simulations based on real-world traffic traces show that the proposed solution can match 10Gbps line rate for executing both PF and CF with the similar costs as CF task only.

Liang Sheng - One of the best experts on this subject based on the ideXlab platform.

  • A Framework of Content Filter Based on Transparent Web Cache
    Computer Engineering, 2004
    Co-Authors: Liang Sheng
    Abstract:

    The network real-time Content Filtering technology has been used widely and developed in our life. Beginning with the shortages of real-time Content Filtering technology, being combined with the existing network topology structure and network technology application and using advanced network technology, this paper proposed a Content-Filtering framework based on specific network architecture, which assorts with LINUX and SQUID an open source software system. Adopting the approach that Content analysis and net address filter cooperate and time-share, the framework can improve the veracity and real time of Content Filtering technology.