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» Intrusion Detection with Neural Networks
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DSN
2005
IEEE
14 years 1 months ago
The Effects of Algorithmic Diversity on Anomaly Detector Performance
Common practice in anomaly-based intrusion detection assumes that one size fits all: a single anomaly detector should detect all anomalies. Compensation for any performance short...
Kymie M. C. Tan, Roy A. Maxion
RAID
2005
Springer
14 years 1 months ago
FLIPS: Hybrid Adaptive Intrusion Prevention
Intrusion detection systems are fundamentally passive and fail–open. Because their primary task is classification, they do nothing to prevent an attack from succeeding. An intru...
Michael E. Locasto, Ke Wang, Angelos D. Keromytis,...
PST
2004
13 years 9 months ago
A novel visualization technique for network anomaly detection
Visualized information is a technique that can encode large amounts of complex interrelated data, being at the same time easily quantified, manipulated, and processed by a human us...
Iosif-Viorel Onut, Bin Zhu, Ali A. Ghorbani
IEEEARES
2008
IEEE
14 years 2 months ago
Effective Monitoring of a Survivable Distributed Networked Information System
In 2002, DARPA put together a challenging proposition to the research community: demonstrate using an existing information system and available DARPA developed and other COTS tech...
Paul Rubel, Michael Atighetchi, Partha Pratim Pal,...
AINA
2010
IEEE
14 years 29 days ago
The Cost Effective Pre-processing Based NFA Pattern Matching Architecture for NIDS
—Network Intrusion Detection System (NIDS) is a system which can detect network attacks resulted from worms and viruses on the Internet. An efficient pattern matching algorithm p...
Yeim-Kuan Chang, Chen-Rong Chang, Cheng-Chien Su