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2 Repeat Step 1 but replace the 10-minute inactive data and the attack data from Run 1 with the 10-minute inactive data and 10-minute norm data of text editing from Run 2 of the data collection to identify the wavelet change characteristics of the text editing norm 3 Repeat Step 1 but use the 10-minute inactive data and 10-minute norm data of web browsing from Run 3 of the data collection to identify the wavelet change characteristics of the web browsing norm For each attack, each wavelet change characteristic of the attack is examined to see if the same characteristic (the same variable with the same wavelet change) also manifests as the data characteristic of either text editing or web browsing If so, this wavelet change characteristic of the attack is removed from the initial list of the wavelet change characteristics for the attack Removing such attack characteristics of the attack which also appear in either normal use activity produces the nal list of the wavelet change characteristics for the attack Figure 113 summarizes the procedure of discovering the wavelet change characteristics for the attacks As discussed in 8, although the above procedure focuses on the wavelet change characteristics of each attack, the wavelet change characteristics of each normal use activity can also be revealed in a similar manner Ultimately, instead of classifying the activities into two categories of attack and normal use, each individual activity can be considered as a distinct category for identifying each distinct activity for purposes other than cyber attack detection Note that the wavelet change attack characteristics obtained from the above procedure involve the change of the wavelet signal strength at a given frequency band from the inactive condition to an attack condition Other kinds of wavelet change characteristics, such as the change of the wavelet form from the inactive condition to the attack condition, are not covered As discussed in 9, the spike, random uctuation, step change, steady change and sinecosine with noise data patterns, with which the wavelet forms are associated, can be linked to the skewed, normal, multimodal, uniform distributions Hence, the distribution change attack
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Figure 113 The procedure of discovering mean shift characteristics for eleven attacks
characteristics described in 9 can be used to gain insights into the change of the wavelet form from the inactive condition to the attack condition
113 WAVELET CHANGE ATTACK CHARACTERISTICS
In Section 1131, some examples of the attack characteristics in wavelet change are illustrated and explained In Section 1132, the ndings of the wavelet change attack characteristics by attacks and by Windows performance objects are presented In Section 1133, the attack groupings based on the same and opposite attack characteristics among the attacks are presented and discussed In Section 1134, the unique attack characteristics are summarized
1131 Examples of wavelet change attack characteristics
Table 111 gives some examples of the attack characteristics in wavelet change For example, under the ARP Poison attack, there is a wavelet change attack characteristic of WDL in
Wavelet change characteristics
Process( Total)\Page Faults/sec This data variable measures the rate at which page faults occur in the threads of this process A page fault occurs when a thread refers to a virtual memory page that is not in its working set in the main memory The signal strength of the DoG wavelet transform at the low frequency band decreases from the inactive condition to the attack condition The DoG wavelet signals of the random page faults at the low frequencies, which appear under the inactive condition, are reduced under the attack possibly because the intensive, repetitive ARP replies from the attacker to the victim computer keep the victim computer busy in responding to them This leaves less resource available to run the background processes that occur in the inactive condition and produce the DoG wavelet signals at the low frequency band