A Survey of Outlier Detection Methods in Network Anomaly Identification <s> An outlier detection algorithm may need access to certain information to work. A labelled training <s> Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?. <s> BIB001 </s> A Survey of Outlier Detection Methods in Network Anomaly Identification <s> An outlier detection algorithm may need access to certain information to work. A labelled training <s> We introduce a probability model for populations of cells and viruses that interact in the presence of an anti-viral agent. Cells can be infected by viruses, and their longevity and ability to avoid infection are modified if they survive successive attacks by viruses. Viruses that survive the effect of the anti-viral agent may find that their ability to survive a future encounter with molecules of the anti-viral agent is modified, as is their ability to infect a healthy cell. Additionally, we assume that the anti-viral agents can be a cocktail with different proportions of agents that target different strains of the virus. In this paper, we give the state equations for the model and derive its analytical solution in steady state. The solution then provides insight into the appropriate mix or ''cocktail'' of anti-viral agents that can be designed to deal with the virus' ability to mutate. In particular, the analysis shows that the concentration of anti-viral agent by itself does not suffice to ultimately control the infection, and that it is important to dose a mix of anti-viral agents so as to target each strain of virus in a specific manner, taking into account the ability of each virus strain to survive in the presence of the anti-viral agents. Models of this kind may eventually lead to the computer aided design of therapeutic protocols or drug design. <s> BIB002
The Computer Journal, Vol. ??, No. ??, ???? data set is one such piece of information that can be used with techniques from machine learning  and statistical learning theory BIB001 ). A training data set is required by techniques which build an explicit predictive model. The labels associated with a data instance denote if that instance is normal or outlier. Based on the extent to which these labels are available or utilized, outlier detection techniques can be either supervised or unsupervised. Supervised outlier detection techniques assume the availability of a training data set which has labelled instances for the normal as well as the outlier class. In such techniques, predictive models are built for both normal and outlier classes. Any unseen data instance is compared against the two models to determine which class it belongs to. An unsupervised outlier detection technique makes no assumption about the availability of labelled training data. Thus, these techniques are more widely applicable. The techniques in this class make other assumptions about the data. For example, parametric statistical techniques assume a parametric distribution for one or both classes of instances. Several techniques make the basic assumption that normal instances are far more frequent than outliers. Thus a frequently occurring pattern is typically considered normal while a rare occurrence is an outlier. Outlier detection is of interest in many practical applications. For example, an unusual flow of network packets, revealed by analysing system logs, may be classified as an outlier, because it may be a virus attack BIB002 or an attempt at an intrusion. Another example is automatic systems for preventing fraudulent use of credit cards. These systems detect unusual transactions and may block such transactions in early stages, preventing, large losses. The problem of outlier detection typically arises in the context of very high dimensional data sets. However, much of the recent work on finding outliers uses methods which make implicit assumptions regarding relatively low dimensionality of the data. A specific point to note in outlier detection is that the great majority of objects analysed are not outliers. Moreover, in many cases, it is not a priori known what objects are outliers.