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An anomaly is defined as something different, abnormal, peculiar or not easily classified While this concept can be applied to virtually anything, we are interested in how it applies to computer security In this context, an anomaly can be defined as some action or data that is not considered normal for a given system, user or network This definition still covers a fairly wide range and can include such things as traffic patterns, user activity and application behavior The belief is that at least a significant portion of threats or concerning conditions should manifest themselves as anomalies and thus be detectable The general approach used by anomaly detection is that something (ie, a network, a host, a set of users, etc) is observed and compared against expected behavior If variation from the expected is noted, that variation is flagged as an anomaly Often this includes some measure of the scope or nature of the variation and serves to drive further investigation, alerting, and analysis One of the key differences between anomaly detection and other forms of detection is that, rather than defining "what is not allowed or bad," it defines "what is allowed or good" Many traditional forms of detection rely on comparing observed behavior and noting when something known to be "bad" is seen These are often referred to as misuse detection systems, however, this nomenclature can be confusing since anomaly systems also detect misuse A more accurate name, perhaps, is explicit detection systems These systems operate well when the number of possible bad behaviors is small and does not change very rapidly However, in larger systems with greater variation, these two conditions often do not hold It then becomes a very onerous task to maintain the list of what is bad Anomaly detection relies on having some definition of allowed behavior and then noting when observed behaviors differ Often additional information about the deviation is noted including its nature, size and frequency in order to further understand what the anomaly is This operates well when it is easier or more efficient to define what is allowed rather than what is not allowed In these cases, the definition of what is allowed tends to be much shorter It also tends not to require changes as new problems are created or discovered Naturally, however, some problems are more applicable than others to this type of approach In particular, certain considerations may alter the effectiveness of anomaly detection for a specific problem Since it is detecting things in a more general way, an anomaly detection system may not be able to identify a problem as precisely as an explicit system It is also important to consider the space that is being observed for anomalies If it is too large, variant and complex, it may be too difficult to describe problems in a manner general enough to make anomaly detection easible (eg, general human behavior) Fortunately, many computer and network problems tend not to fall into this category which is what makes anomaly detection such an attractive technique Anomaly detection systems monitor networks for two primary criteria, characteristic deviation and statistical deviation While it can certainly be argued that there are more (or less) ways to classify such systems, these two divisions provide a convenient manner in which to discuss the various implementations Characteristic deviations tend to be more qualitative For example, "User joe123 does not normally use transfer files outside of the company" Statistical deviations, on the other hand, tend to be more quantitative For example, "This site's ICMP traffic never exceeds 15 percent of capacity".

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17 presented tools that intruders use to confuse and evade NSM operations We learned about applications that intruders employ to perform reconnaissance, exploitation, and other nefarious deeds This chapter discusses strategies and tactics that intruders use to accomplish the same goal Here the targets are the people and processes that perform NSM operations While this chapter mentions tools, they are not analyzed to the extent they were in 17 Rather, tools are discussed if they represent a technique to defeat the eople and processes that detect intrusions 1 introduced the idea of structured and unstructured threats This chapter applies more to the former than the latter Structured threats are very concerned with quietly compromising a victim and maintaining the unauthorized access They will employ one or more of the techniques described here to ensure an NSM operation fails to discover their presence Unstructured threats typically do not care if they are discovered If the victim detects their presence, unstructured threats move on to the next ill-prepared organization Structured threats typically compromise specific victims to achieve well-defined goals like financial crime or theft of intellectual property Unstructured threats value access, bandwidth, and hard drive space Systems with plenty of each are great places to store pirated software, trade music, or launch bandwidth consumption attacks against rival Internet gang members Because the line between structured and unstructured threats blurs, some of this chapter's tactics are employed by less sophisticated intruders This chapter is divided into four major sections about attackers In order of increasing sophistication, an intruder wishes to promote anonymity, evade detection, or, best of all, simply appear normal If all else fails, the intruder will degrade or deny collection of evidence, which complicates network- and host-based investigations I examine where attackers come from, what they do, and how they do it I explain many of these techniques in light of the five phases of compromise introduced in 1 (reconnaissance, exploitation, reinforcement, consolidation, and pillage) I conclude with a fifth section that describes poor NSM operational choices and processes Managers influence this realm, but intruders exploit the consequences of poor management, inadequate funding, and a lack of training.