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A high-performance network intrusion detection system

Published: 01 November 1999 Publication History

Abstract

In this paper we present a new approach for network intrusion detection based on concise specifications that characterize normal and abnormal network packet sequences. Our specification language is geared for a robust network intrusion detection by enforcing a strict type discipline via a combination of static and dynamic type checking. Unlike most previous approaches in network intrusion detection, our approach can easily support new network protocols as information relating to the protocols are not hard-coded into the system. Instead, we simply add suitable type definitions in the specifications and define intrusion patterns on these types. We compile these specifications into a high-performance network intrusion detection system. Important components of our approach include efficient algorithms for pattern-matching and information aggregation on sequences of network packets. In particular, our techniques ensure that the matching time is insensitive to the number of patterns characterizing different network intrusions, and that the aggregation operations typically take constant time per packet. Our system participated in an intrusion detection evaluation organized by MIT Lincoln Labs, where our system demonstrated its effectiveness (96% detection rate on low-level network attacks) and performance (real-time detection at 500Mbps), while producing very few false positives (0.05 to 0.1 per attack).

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cover image ACM Conferences
CCS '99: Proceedings of the 6th ACM conference on Computer and communications security
November 1999
160 pages
ISBN:1581131488
DOI:10.1145/319709
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 November 1999

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CCS99: Sixth ACM Conference on Computer and Communication Security
November 1 - 4, 1999
Kent Ridge Digital Labs, Singapore

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Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

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  • (2023)Hyper Parameter Optimized NIDS via Machine Learning in IoT Ecosystem2023 IEEE 2nd International Conference on Industrial Electronics: Developments & Applications (ICIDeA)10.1109/ICIDeA59866.2023.10295218(499-504)Online publication date: 29-Sep-2023
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