APPLICATION OF CCAS TO CYBER ATTACK DETECTION in .NET

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123 APPLICATION OF CCAS TO CYBER ATTACK DETECTION
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Only the information of event type is extracted from each audit record to distinguish attack activities from normal use activities There are in total 284 different event types that BSM can record from a Solaris system Given a series of audit events which are recorded in the audit data and represented by their event types, we obtain a smoothed frequency distribution of 284 event types in the recent past of each given event For the nth event in the series of audit events, the
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Clustering and classifying attack and normal use data
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smoothed frequency distribution of 284 events in the recent past of this nth event is computed as follows: xi (n) = z + (1 ) xi (n 1) , where z = 1 if the nth event has event type i; 0 otherwise, xi (0) = 0, and is set to 03 Formula 1213 produces an Exponentially Weighted Moving Average (EWMA) [8] of the event frequency in the recent past of the nth event for each event type For example, when computing the smoothed event frequency of event type i for the nth event, the presence (counted as 1) of event type i in the nth event is given the weight of , the presence of event type i in the (n 1)th event is given the weight of (1 ), the presence of event type i in the (n 2)th event is given the weight of (1 )2 , and so on Hence, each audit event in the training data for each input order is transformed into a smoothed event frequency vector, (x1 , , x284 ), which is considered a data point in the 284dimensional space Each audit event in the testing data is transformed into a smoothed event frequency vector CCAS is applied to all the data points in the training data set The target value of each data point in the testing data set is determined using Formula 1211 based on the clusters learned using CCAS To evaluate the detection accuracy of CCAS for the testing data, the session signal ratio is rst determined for each attack session and each normal use session in the testing data as follows: 1 Set an event signal threshold to evaluate the target value of the data point for each audit event in the testing data as follows: Event signal threshold = + a where is the average target value of the data points for all the normal use events in the training data, is the standard deviation of the target values of these data points, and a is a parameter which is determined empirically Numerous values of a have been tested, and the small values of 05, 06 and 07 appear to produce the best detection performance 2 Compare the target value of the data point for each audit event in the testing data set with the event signal threshold, and signal the event as attack if the target value is not smaller than the signal threshold or claim the event as normal use otherwise Note that a larger target value close to 1 indicates the likelihood of an attack event since the target class of an attack event is 1 and the target class of a normal use event is 0 3 Compute the session signal ratio for each session by dividing the total number of the signaled attack events by the total number of the audit events in the session 4 Plot the Receiver Operating Characteristic (ROC) chart based on the session signal ratio values of all the sessions in the testing data set in the following steps: (a) Set a session signal threshold to a value which is less than the smallest value of the session signal ratios for all the sessions in the testing data i = 1, , 284, 0 < 1 (1213)
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