The Experts below are selected from a list of 42177 Experts worldwide ranked by ideXlab platform
Baohua Yang - One of the best experts on this subject based on the ideXlab platform.
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Scalable NIDS via Negative Pattern Matching and Exclusive Pattern Matching
2010 Proceedings IEEE INFOCOM, 2010Co-Authors: Kai Zheng, Xin Zhang, Zhijun Wang, Baohua YangAbstract:In this paper, we identify the unique challenges in deploying parallelism on TCAM-based Pattern Matching for Network Intrusion Detection Systems (NIDSes). We resolve two critical issues when designing scalable parallelism specifically for Pattern Matching modules: 1) how to enable fine-grained parallelism in pursuit of effective load balancing and desirable speedup simultaneously; and 2) how to reconcile the tension between parallel processing speedup and prohibitive TCAM power consumption. To this end, we first propose the novel concept of Negative Pattern Matching to partition flows, by which the number of TCAM lookups can be significantly reduced, and the resulting (fine-grained) flow segments can be inspected in parallel without incurring false negatives. Then we propose the notion of Exclusive Pattern Matching to divide the entire Pattern set into multiple subsets which can later be matched against selectively and independently without affecting the correctness. We show that Exclusive Pattern Matching enables the adoption of smaller and faster TCAM blocks and improves both the Pattern Matching speed and scalability. Finally, our theoretical and experimental results validate that the above two concepts are inherently complementary, enabling our integrated scheme to provide performance gain in any scenario (with either clean or dirty traffic).
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INFOCOM - Scalable NIDS via Negative Pattern Matching and Exclusive Pattern Matching
2010 Proceedings IEEE INFOCOM, 2010Co-Authors: Kai Zheng, Xin Zhang, Zhijun Wang, Baohua YangAbstract:In this paper, we identify the unique challenges in deploying parallelism on TCAM-based Pattern Matching for Network Intrusion Detection Systems (NIDSes). We resolve two critical issues when designing scalable parallelism specifically for Pattern Matching modules: 1) how to enable fine-grained parallelism in pursuit of effective load balancing and desirable speedup simultaneously; and 2) how to reconcile the tension between parallel processing speedup and prohibitive TCAM power consumption. To this end, we first propose the novel concept of Negative Pattern Matching to partition flows, by which the number of TCAM lookups can be significantly reduced, and the resulting (fine-grained) flow segments can be inspected in parallel without incurring false negatives. Then we propose the notion of Exclusive Pattern Matching to divide the entire Pattern set into multiple subsets which can later be matched against selectively and independently without affecting the correctness. We show that Exclusive Pattern Matching enables the adoption of smaller and faster TCAM blocks and improves both the Pattern Matching speed and scalability. Finally, our theoretical and experimental results validate that the above two concepts are inherently complementary, enabling our integrated scheme to provide performance gain in any scenario (with either clean or dirty traffic).
Kai Zheng - One of the best experts on this subject based on the ideXlab platform.
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Scalable NIDS via Negative Pattern Matching and Exclusive Pattern Matching
2010 Proceedings IEEE INFOCOM, 2010Co-Authors: Kai Zheng, Xin Zhang, Zhijun Wang, Baohua YangAbstract:In this paper, we identify the unique challenges in deploying parallelism on TCAM-based Pattern Matching for Network Intrusion Detection Systems (NIDSes). We resolve two critical issues when designing scalable parallelism specifically for Pattern Matching modules: 1) how to enable fine-grained parallelism in pursuit of effective load balancing and desirable speedup simultaneously; and 2) how to reconcile the tension between parallel processing speedup and prohibitive TCAM power consumption. To this end, we first propose the novel concept of Negative Pattern Matching to partition flows, by which the number of TCAM lookups can be significantly reduced, and the resulting (fine-grained) flow segments can be inspected in parallel without incurring false negatives. Then we propose the notion of Exclusive Pattern Matching to divide the entire Pattern set into multiple subsets which can later be matched against selectively and independently without affecting the correctness. We show that Exclusive Pattern Matching enables the adoption of smaller and faster TCAM blocks and improves both the Pattern Matching speed and scalability. Finally, our theoretical and experimental results validate that the above two concepts are inherently complementary, enabling our integrated scheme to provide performance gain in any scenario (with either clean or dirty traffic).
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INFOCOM - Scalable NIDS via Negative Pattern Matching and Exclusive Pattern Matching
2010 Proceedings IEEE INFOCOM, 2010Co-Authors: Kai Zheng, Xin Zhang, Zhijun Wang, Baohua YangAbstract:In this paper, we identify the unique challenges in deploying parallelism on TCAM-based Pattern Matching for Network Intrusion Detection Systems (NIDSes). We resolve two critical issues when designing scalable parallelism specifically for Pattern Matching modules: 1) how to enable fine-grained parallelism in pursuit of effective load balancing and desirable speedup simultaneously; and 2) how to reconcile the tension between parallel processing speedup and prohibitive TCAM power consumption. To this end, we first propose the novel concept of Negative Pattern Matching to partition flows, by which the number of TCAM lookups can be significantly reduced, and the resulting (fine-grained) flow segments can be inspected in parallel without incurring false negatives. Then we propose the notion of Exclusive Pattern Matching to divide the entire Pattern set into multiple subsets which can later be matched against selectively and independently without affecting the correctness. We show that Exclusive Pattern Matching enables the adoption of smaller and faster TCAM blocks and improves both the Pattern Matching speed and scalability. Finally, our theoretical and experimental results validate that the above two concepts are inherently complementary, enabling our integrated scheme to provide performance gain in any scenario (with either clean or dirty traffic).
I. V. Ramakrishnan - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Pattern Matching
SIAM Journal on Computing, 1995Co-Authors: R. C. Sekar, R. Ramesh, I. V. RamakrishnanAbstract:Pattern Matching is an important operation used in many applications such as functional programming, rewriting, and rule-based expert systems. By preprocessing the Patterns into a DFA-like automaton, we can rapidly select the Matching Pattern(s) in a single scan of the relevant portions of the input term. This automaton is typically based on left-to-right traversal of the Patterns. By adapting the traversal order to suit the set of input Patterns, it is possible to considerably reduce the space and Matching time requirements of the automaton. The design of such adaptive automata is the focus of this paper. We first formalize the notion of an adaptive traversal. We then present several strategies for synthesizing adaptive traversal orders aimed at reducing space and Matching time complexity. In the worst case, however, the space requirements can be exponential in the size of the Patterns. We show this by establishing an exponential lower bounds on space that is independent of the traversal order used. We then discuss an orthogonal approach to space minimization based on direct construction of optimal dag automata. Finally, our work brings forth the impact of typing in Pattern Matching. In particular, we show that several important problems (e.g., lazy Pattern Matching in ML) are computationally hard in the presence of type disciplines, whereas they can be solved efficiently in the untyped setting.
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ICALP - Adaptive Pattern Matching
Automata Languages and Programming, 1992Co-Authors: R. C. Sekar, R. Ramesh, I. V. RamakrishnanAbstract:Pattern Matching is an important operation used in many applications such as functional programming, rewriting and rule-based expert systems. By preprocessing the Patterns into a DFA-like automaton, we can rapidly select the Matching Pattern(s) in a single scan of the relevant portions of the input term. This automaton is typically based on left-to-right traversal (of the Patterns) or its variants. By adapting the traversal order to suit the set of input Patterns, it is possible to considerably reduce the space and Matching time requirements of the automaton. The design of such adaptive automata is the focus of this paper. In this context we study several important problems that have remained open even for automata based on left-to-right traversais. Such problems include upper and lower bounds on space complexity, construction of optimal dag automata and impact of typing in Pattern Matching. An interesting consequence of our results is that lazy Pattern Matching in typed systems (such as ML) is computationally hard whereas it can be done efficiently in untyped systems.
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Nonlinear Pattern Matching in trees
Journal of the ACM, 1992Co-Authors: R. Ramesh, I. V. RamakrishnanAbstract:Tree Pattern Matching is a fundamental operation that is used in a number of programming tasks such as mechanical theorem proving, term rewriting, symbolic computation, and nonprocedural programming languages. In this paper, we present new sequential algorithms for nonlinear Pattern Matching in trees. Our algorithm improves upon know tree Pattern Matching algorithms in important aspects such as time performance, ease of integration with several reduction strategies and ability to avoid unnecessary computation steps on match attempts that fail. The expected time complexity of our algorithm is linear in the sum of the sizes of the two trees.
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Parallel tree Pattern Matching
Journal of Symbolic Computation, 1990Co-Authors: R. Ramesh, I. V. RamakrishnanAbstract:Tree Pattern Matching is a fundamental operation that is used in a number of programming tasks such as code optimization in compilers, symbolic computation, automatic theorem proving and term rewriting. An important special case of this operation is linear tree Pattern Matching in which an instance of any variable in the Pattern occurs at most once. In this paper we describe a new parallel algorithm for linear tree Pattern Matching using a parallel random access machine model.
Uznaskiprzemysaw - One of the best experts on this subject based on the ideXlab platform.
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Order-preserving Pattern Matching with k mismatches
Theoretical Computer Science, 2016Co-Authors: Gawrychowskipawe, UznaskiprzemysawAbstract:We consider a generalization of the recently introduced order-preserving Pattern Matching. The difference between the standard Pattern Matching and the order-preserving variant is that instead of l...
M. Sohel Rahman - One of the best experts on this subject based on the ideXlab platform.
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Order preserving Pattern Matching revisited
Pattern Recognition Letters, 2015Co-Authors: Mahbubul Hasan, A. S. M. Shohidull Islam, Mohammad Saifur Rahman, M. Sohel RahmanAbstract:We study the order preserving Pattern Matching problem.We focus on string regularities from an order preserving point of view.We also present yet another efficient order preserving Pattern Matching algorithm.We modify the Z-algorithm to make it useful in the order preserving framework. In this paper, we study the order preserving Pattern Matching (OPPM) problem, which is a very recent variant of the classic Pattern Matching problem. We revisit this variant, present a new interesting Pattern Matching algorithm and for the first time consider string regularities from this new perspective.
