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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 distribution change characteristics of the web browsing norm The test procedure is not applied to the FTP buffer over ow attack due to the short duration of this attack and too few data observations obtained under this attack For each attack, each distribution change characteristic of the attack is examined to see if the same characteristic (the same variable with the same distribution change) also manifests as the norm characteristic of either text editing or web browsing If so, this distribution change characteristic of the attack is removed from the initial list of the attack characteristics Removing such attack characteristics which also appear in either normal use activity produces the nal list of the distribution change characteristics for the attack Figure 93 summarizes the procedure of discovering the distribution change characteristics for the attacks As discussed in 8, although the above procedure focuses on the distribution change characteristics of the attacks, the distribution change characteristics for the text editing and the web browsing 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 distinctive category to identify each distinctive activity for purposes other than cyber attack detection
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Two samples of inactive data and attack 1 data from Run 1
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Two samples of inactive data and attack 2 data from Run 1
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Two samples of inactive data and attack 10 data from Run 1
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Two samples of inactive data and text editing data from Run 2
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Initial list of distribution change characteristics for attack 1
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Initial list of distribution change characteristics for text editing
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Final list of distribution change characteristics for attack 1
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Final list of distribution change characteristics for attack 2
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Figure 93 The procedure of discovering distribution change attack characteristics
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94 DISTRIBUTION CHANGE ATTACK CHARACTERISTICS
Section 941 shows the percentages of the ve probability distribution types under the 11 attack conditions and the two normal use conditions In Section 942, some examples of the attack characteristics in probability distribution changes are illustrated and explained In Section 943, the ndings of the distribution change attack characteristics by attacks and by Windows performance objects are presented In Section 944, the attack groupings based on the same and opposite attack characteristics among the attacks are presented and discussed In Section 945, the unique attack characteristics are summarized
941 Percentages of the probability distributions under the attack and normal use conditions
For each condition (attack or normal use), the percentage of the data variables with each of the ve types of probability distributions is calculated, and is shown in Table 92 For all the attacks and the normal use activities, the skewed distribution and the multimodal distribution are the most dominant probability distributions, accounting for 4337% (the sum of 3719% for the right skewed distribution and 618% for the left skewed distribution) and 4222% of the data variables in average A large majority of the variables with the skewed distribution are right skewed with dominantly the upward spikes The unimodal symmetric distribution accounts for 878% of the variables in average across all the attack and normal use activities, which is a little more than 563% of the variables with the uniform distribution The dominance of the multimodal and right skewed distributions and the small percentages of the left skewed, unimodal symmetric and uniform distributions are found consistently in both the attacks and the normal use activities
Table 92 The percentages of probability distributions under attack and normal use conditions Types of probability distributions (%) Total number of variables 350 337 322 327 349 322 440 480 418 492 382 483 Left Skewed (DUL) 314 000 932 765 201 683 1023 313 1100 772 628 683 618 Right Skewed (DUR) 4171 1157 3602 2905 3181 3230 4455 4688 4019 4858 4058 4306 3719 Unimodal Symmetric (DUS) 771 3828 342 703 516 745 1068 521 407 163 1021 455 878 Uniform (DUF) 000 1128 1242 153 1891 186 045 083 837 589 602 000 563 Multimodal (DMM) 4743 3887 3882 5474 4212 5155 3409 4396 3636 3618 3691 4555 4222
Activity Apache ARP Distributed Fork Hardware Remote Rootkit Security Software Vulnerability Text Editing Web Browsing Average
Distribution change attack characteristics Table 93 Examples of distribution change attack characteristics Attacks Variables Apache ARP Distributed DUS DUR DUF DUR DUR DUS Remote Rootkit DUS Security