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Richard P. Lippmann
Researcher at Massachusetts Institute of Technology
Publications - 93
Citations - 22318
Richard P. Lippmann is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Artificial neural network & Intrusion detection system. The author has an hindex of 43, co-authored 92 publications receiving 21619 citations.
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Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Journal ArticleDOI
An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Proceedings ArticleDOI
Automated generation and analysis of attack graphs
TL;DR: This paper presents an automated technique for generating and analyzing attack graphs, based on symbolic model checking algorithms, letting us construct attack graphs automatically and efficiently.
Journal ArticleDOI
Neural Network Classifiers Estimate Bayesian a posteriori Probabilities.
TL;DR: Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically demonstrate that network outputs provide good estimates of Bayesian probabilities.
Journal ArticleDOI
The 1999 DARPA off-line intrusion detection evaluation
TL;DR: This report describes new and known approaches and strategies that were used to make attacks stealthy for the 1999 DARPA Intrusion Detection Evaluation, and includes many examples of stealthy scripts that can be use to implement stealthy procedures.