There is no universally accepted definition of a neural network. Most definitions, however, agree that they are networks of many simple processors or units that are connected and process numeric values. Neural networks are models of biological learning systems; in fact, much of the inspiration in all the fields of AI comes from the desire of researchers to emulate with software the human capacities of recognition, learning, remembering, and evolving. Thus, neural networks were developed as analogs of human brains. They were proposed 50 years ago in theory, motivated by a desire by scientists to understand how the human brain works. Similar to the way brain cells learn, neural networks work through a process of excitation and connection, depending on the weighted functions of the inputs from many other cells to which they are wired.
A neural network software system is an information-processing program inspired by the heavily interconnected structure of the human brain. They are collections of mathematical models that emulate some of the observed properties of biological nervous systems and draw on the analogies of adaptive biological learning. Learning in biological systems involves adjustments of the synaptic connections that exist between the neurons. This is the basic process that neural networks attempt to replicate; evenly distributed observations are required for the network to learn the patterns of different types of behavior. Learning occurs by example through exposure to a set of input-output data where the training algorithm iteratively adjusts the connection weights (synapses). These connection weights store the knowledge necessary to solve specific problems, which, for investigative data mining, involve recognizing the patterns of various types of cybercrimes, fraud, system intrusions, and other digital crimes (Fausett, 1994).
Knowledge, then, for a neural network is reduced to a set of weights between its internal connections. Learning comes down to what gets encoded in the wiring and the weighting factors of the various neurons. For example, to construct a fraud-detection model with a neural network, samples of fraudulent and non-fraudulent transactions are required for it to distinguish the different features and behavior of each. For this reason, when working with neural networks, the selection of samples in the training is extremely important. Adequate effort must be made to ensure that a balanced number of observations are presented to a network.