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Most security-focused anomaly systems tend to fall into one of three general categories: behavioral, traffic pattern or protocol Systems that look for anomalies in behavior (usually user behavior) patterns are considered behavioral anomaly systems These are primarily characteristic systems, though they may cover some statistical criteria, such as what types of applications and protocols are used at various times of day, relationships between source and destination of network activity, or even what types of email attachments are being sent through a system For example, consider some of the credit fraud systems used to monitor credit card usage The advantage of anomaly detection here is that it can be constructed to detect very subtle qualitative anomalies However, creating a model that is neither subject to excessive false positives (when a user just changes their behavior) nor vulnerable to gradual skewing by an attacker is difficult Systems that look for anomalies in network traffic patterns are considered traffic pattern anomaly systems These are primarily statistical in nature, though they may include some characteristics, such as volume of traffic, mix of protocol and various measures of source and destination distributions To illustrate, consider some network management or simple denial-of-service monitoring systems, which have the advantage of operating on a much larger and variant domain and can build upon a number of good statistical models However, their disadvantage is that they are often unable to detect subtle quantitative or most qualitative anomalies They also present some difficulties in defining a reliable baseline upon which to perform the statistical analysis Systems that look for anomalies in protocols are considered protocol anomaly systems Primarily characteristic systems, these tend to vary a bit depending on the implementation but the most effective are often implemented as strict model systems This type of design takes advantage of the fact that protocols by themselves are usually very restrictive They tend to severely limit the nature and order of transactions and are often very well described by some reference implementation or document As such, it is possible to construct a very strict model of what should occur and easily note any deviation from this model A further advantage of this system is that it can detect a very wide range of anomalies within the protocol space and can be very efficiently constructed The disadvantages, however, are that it may be difficult to accurately estimate the effect of the anomaly observed and that some types of problematic protocol transactions (ie, attacks) do not manifest themselves as anomalies
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There are many means of implementing protocol anomaly detection Most of the variation depends on how much detail is monitored and how much state is maintained Protocol anomaly detection is built on a foundation of pattern matching What differentiates it from explicit matching (ie, signature systems) is the kind of patterns used In most cases, protocol anomaly detection also requires some sort of stateful protocol-aware matching system, without which it can be very prone to false positives While a very simplistic implementation of protocol anomaly detection may only look for a small number of known problematic conditions, such as overlong buffers and questionable encoding, a more complete implementation can track every transition and evaluate all data for compliance The tradeoff is typically in speed or execution time because the more detail an implementation tracks, the more comparisons it must perform at each stage However, a more complete implementation also allows for detection of a wider range of anomalies From a state maintenance perspective, a very simplistic implementation may maintain some basic state about a given flow, such as open, authenticated and so on More complete implementations, however, can maintain a complete transactional history of a given flow The tradeoff here is in storage capacity More state requires more storage, but it also allows an implementation to draw more complex conclusions and detect more subtle anomalies One of the most complete forms of protocol anomaly detection is application protocol modelling In this form, a model of a given protocol is created from the protocol specification and some study of the various implementations This model is then used by a system designed to track a protocol flow and compare it to the model While it is possible to perform protocol anomaly detection in many stateful, protocol-aware systems (ie, "stateful inspection engines"), the limitations on the amount of state stored and the patterns matched often create a fairly lightweight implementation This is usually the result of an attempt to use a framework designed for explicit matching for anomaly detection While it can generate some results, it will never be as complete as something designed for the type of matching used by an anomaly detection system
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