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as our knowledge about attacks grows over time, especially when new attacks appear and then are identi ed to add new attack data models The detection models based on the attack norm separation methodology can be used in parallel with the detection models based on the anomaly detection methodology to reap the bene ts of both The signature recognition methodology is similar to the attack norm separation methodology in using speci c attack data models to both detect and identify an attack However, the signature recognition methodology lacks the capability to handle the mixed attack and normal use data as discussed in Part IV In addition to the additive mixture, there are many other ways in which attack data and normal use data can join together For example, if the multiplicative mixture of attack data and normal use data is assumed as follows: yt = f (xt )g(xt ) + t , (1711)
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and = 0 = 1/ g(xt ) when no attack is present The cuscore takes the following form:
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Q0 =
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t0 dt0 =
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[yt f (xt )] f (xt )g(xt ),
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(1712)
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because t0 = yt f (xt )| = dt0 = f (xt )g(xt )| = 1 g(xt ) 1 g(xt ) (1713) (1714)
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When = 0, Q 0 should uctuate around zero; otherwise, the Q 0 values move upward or downward consistently, depending on the positive or negative sign of [yt f (xt )] f (xt )g(xt ) Given the attack data, the normal use data and the mixed attack and normal use data samples, the attack data model, the normal use data model and even the mixed attack-norm data model can be de ned However, the mixture type of the attack data and the normal use data is still unknown, and it is a challenge to determine how the attack data and the normal use data are mixed together, even though the attack data model, the normal use data model, and the mixed attack-norm data model are given Nevertheless, the mixture type of the attack data and the normal use data must be given in order to derive the cuscore to rst cancel the effect of the normal use data in the mixed attack and normal use data and then detect the presence of the attack in the residual data Research is required to address the problem of identifying the mixture type or model, given the attack data model, the normal use model, and the mixed attack-norm data model
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173 DETECTION PERFORMANCE OF THE CUSCORE DETECTION MODELS
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The Windows performance objects data described in 7 is used to test the application of the cuscore detection models to cyber attack detection In order to compare the detection performance of the cuscore detection models in this chapter, the ANN-based signature
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Detection performance of the cuscore detection models
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recognition models described in 13 and the EWMA control chart-based anomaly detection models described in 14, the three techniques are tested using the same variables and their data which are involved in the attack characteristics in Table 131 Speci cally, two cuscore detection models are developed for each attack characteristic in Table 131 for the attack with that attack characteristic in combination with the text editing norm and the web browsing norm, respectively For example, the ARP Poison attack has the autocorrelation increase (A+) characteristic in Network Interface\Bytes Received/sec The attack data model for this data variable is developed using the attack data from Run 1 of the data collection, and is de ned in Formulas 1610 and 1611 The text editing data model is developed using the rst half of the text editing data with 300 observations from Run 2 of the data collection, and is de ned in Formulas 1612 1614 The web browsing data model is developed using the rst half of the web browsing data with 300 observations from Run 3 of the data collection, and is de ned in Formulas 1616 1617 Since the mixture type or model of the attack data and the normal use data for the variable is not known, a cuscore detection model using the attack data model and each of the two normal use data models is developed based on the additive mixture model in Formula 177 The cuscore detection model based on the additive mixture is tested on the second half of the normal use data with 300 observations and the mixed attack and normal use data from Run 2 or Run 3 of the data collection, depending on which normal use data model is used in the cuscore detection model For a given cuscore detection model, a signal threshold is determined by observing the cuscore values for the data observations in the testing data and selecting a value that produces a small number of false alarms and an early rst hit The description of the false alarms and the rst hit is given in 13 Figures 171 and 172 show the cuscore charts for the two cuscore detection models that are developed for the autocorrelation increase attack characteristic in Network Interface\Bytes Received/sec under the ARP Poison attack Each cuscore chart presents the cuscore values produced by a cuscore detection model for the data observations in the testing data The vertical line in Figure 171 and Figure 172 indicates when the attack begins In both
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