A Review of Anomaly based Intrusion Detection Systems <s> Artificial Neural Network <s> Abstract By replacing the sigmoid activation function often used in neural networks with an exponential function, a probabilistic neural network (PNN) that can compute nonlinear decision boundaries which approach the Bayes optimal is formed. Alternate activation functions having similar properties are also discussed. A fourlayer neural network of the type proposed can map any input pattern to any number of classifications. The decision boundaries can be modified in real-time using new data as they become available, and can be implemented using artificial hardware “neurons” that operate entirely in parallel. Provision is also made for estimating the probability and reliability of a classification as well as making the decision. The technique offers a tremendous speed advantage for problems in which the incremental adaptation time of back propagation is a significant fraction of the total computation time. For one application, the PNN paradigm was 200,000 times faster than back-propagation. <s> BIB001 </s> A Review of Anomaly based Intrusion Detection Systems <s> Artificial Neural Network <s> An anomaly intrusion detection approach based on Hybrid MLP/CNN (Multi-layer Perceptron / Chaotic Neural Network) neural network is proposed in this paper. Most anomaly detection approaches using MLP can detecting novel real-time attacks, but still have high false alarm rates. Most attacks are composed of a series of anomaly events. These attacks are called timedelayed attacks, which current neural network IDSs (Intrusion Detection System) cannot identify efficiently. A hybrid MLP/CNN neural network is constructed in order to improve the detection rate of time-delayed attacks. While obtaining a similarly detection rate of real-time attacks as the MLP does, the proposed approach can detect time-delayed attacks efficiently with chaotic neuron. This approach also exhibits a lower false alarm rate when detects novel attacks. The simulation tests are conducted using DARPA 1998 dataset. The experimental results are presented and compared in ROC curves, which can demonstrate that the proposed approach performs exceptionally in terms of both detection rate and false alarm rate. <s> BIB002
Artificial Neural network (ANN) architectures (popular one being , Multilayer Perceptron (MLP), a layered feed-forward topology in which each unit performs a biased weighted sum of their inputs and pass this activation level through a transfer function to produce their output BIB002 ), are able to identify notreadily-observable patterns, however MLP is ineffective with new data. For general signal processing and pattern recognition problems, another branch of ANN that makes use of radial basis function, called The Modified Probabilistic Neural Network (related to General Regression Neural Network (GRNN) classifier BIB001 and generalization of Probabilistic Neural Network (PNN)), was introduced by Zaknich [28] . It assigns the clusters of input vectors rather than each individual training case to radial units.