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When IDSs were first contemplated in the 1970s and 1980s, many focused on anomaly detection Researchers continue to devote energy to this method, but pattern-based detection has been the dominant strategy for the last decade Pattern- based detection prevailed for a few simple reasons First it was easier for developers to implement Searching a packet or reassembled stream for a string of characters is conceptually simple Ever-increasing traffic loads and deployment of encryption frustrate this approach, but pattern matching survives Second, it is easier for analysts to understand If an analyst knows the detection product is watching for a certain pattern, it makes sense when the IDS reports seeing that pattern If the pattern does not indicate a malicious event occurred, it's not the IDS's fault Verification has been lacking, but NSM approaches and integration of assessment techniques make pattern matching increasingly effective Third, it is much easier to deploy a rule based on a new pattern than it is to implement a new algorithm for anomaly-based intrusion detection Snort rules are released within hours of the discovery of a new worm, exploit, and other malicious code Engineers and analysts can plug the new rule into their product and validate or tune it as necessary Anomaly detection is not dead, however Pattern-matching systems are frequently compared to signature-based antivirus products If an antivirus product strictly relied on patterns to intercept and defeat malware, users would suffer infections much more often Antivirus products supplement their pattern-matching features with heuristic detection algorithms Heuristic techniques concentrate more on the acts the malware take, like opening network sockets, sending e-mail, and so on, and less on the code used to bring these acts to life Implementing heuristic detection or anomaly-based detection requires knowing what is normal, suspicious, and malicious We encountered those terms earlier in the book with respect to manual inspection of security events In some sense a human analyst is an anomaly detection device An experienced human analyst develops a sense of what is normal for the organizations he or she monitors Seeing an alert or session that falls outside those boundaries of normality prompts the analyst to investigate Validating these unusual events requires access to high-fidelity network traffic, as is provided by session data and full content data Network profiling is related to anomaly detection It's the idea that organizations should develop a formal sense of the devices and traffic patterns required for normal operational use Introduction of new devices or traffic types should raise an alarm Marcus Ranum calls this technique first seen anomaly detection: "[A first seen anomaly detection] system treats the first time it sees an instance of a thing as an anomaly It's accurate that something never seen before is always anomalous, in at least a few senses of the word When I was looking at statistical anomaly detection for IDS I realized that the first seen anomaly was 99% of what you'd get [with statistical anomaly detection products]".

For the collected data of the Windows performance objects described in 7, the same data screening procedure as described in Section 82 is performed to eliminate the data variables which have the observations of the same value under all three conditions: the inactive, attack and norm conditions Each of the remaining data variables is analyzed to extract the wavelet feature and discover the wavelet change characteristics of attack and normal use data For the data sample of a given data variable under each condition (inactive, attack and norm) of the collected data, the wavelet transform is performed using each of the ve wavelet forms The statistical toolbox of MATLAB Version 650180913a (R13) is used to perform the wavelet transforms and obtain the wavelet coef cients For the wavelet transform using the Haar and Daubechies wavelets, the k value of 8 is applied to a data sample of 256 ata observations Three frequency bands are de ned with the low frequency band containing the three lowest frequencies, the high frequency band containing the three highest frequencies, and the medium frequency band containing the remaining two frequencies [6] For the Paul, DoG and Morelet wavelet transforms applied to each data variable, there are 29 frequencies for 256 data observations These frequencies are considered to fall into three frequency bands: the low frequency band containing the eight lowest frequencies, the high frequency band containing the twelve highest frequencies, and the medium frequency band containing the remaining nine frequencies [6] For each wavelet transform of each variable under each condition (inactive, attack and norm), the Signal Strength (SS) at each frequency band is computed using the wavelet coef cients at that frequency as follows: SS = 1 n.

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where w j is a wavelet coef cient and n is the total number of wavelet coef cients at that frequency band The analysis of variance (ANOVA) is then carried out in the following steps: 1 For the 10-minute data under the inactive condition and the attack data for the entire attack period from Run 1 of the data collection, perform an ANOVA with two independent variables of frequency band and condition and the dependent variable of the signal strength The condition has two levels: inactive and attack The frequency band has three levels: low, medium, and high The SS value for a frequency in a given frequency band under a given condition is a data observation for that combination of the frequency and the condition For example, there are eight data observations of SS from a Haar transform of a data variable under a given condition The ANOVA test along with the Tukey test, which is carried out using the statistical toolbox of MATLAB, reveals whether or not there is a signi cant difference or change of SS from the inactive condition to the attack condition at each frequency band If there is a signi cant change of the signal strength at a given frequency band, this change of the wavelet signal strength is considered a wavelet change attack characteristic For example, if the signal strength from the Haar transform of a iven data variable at the low frequency has a signi cant increase from the inactive condition to.

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