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Tables 912 to 916 provide the number of distribution changes to the multimodal, uniform, unimodal symmetric, right skewed, left skewed distributions, respectively, which are unique to each attack For example, for the Memory object, the Fork Bomb attack has one unique attack characteristic of distribution change to the multimodal distribution in Memory\System Cache Resident Bytes, which does not appear in the other attacks Since Memory\System Cache Resident Bytes also shows the change in distribution change to the right skewed distribution under the Vulnerability Scan attack, the two attack characteristics also account for one different attack characteristic between the two attacks in Table 911
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Table 912 The number of unique attack characteristics of distribution change to the multimodal distribution for each attack Attacks Distributed 1 ARP Hardware Security Vulnerability FORK Rootkit Software Remote 1
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Table 913 The number of unique attack characteristics of distribution change to the uniform distribution for each attack Attacks Distributed ARP Hardware Security Vulnerability FORK Rootkit Software Remote
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Table 914 The number of unique attack characteristics of distribution change to the unimodal symmetric distribution for each attack Attacks Distributed ARP Hardware Security Vulnerability FORK Rootkit Software Remote
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Table 915 The number of unique attack characteristics of distribution change to the right skewed distribution for each attack Attacks Distributed 2 4 4 2 2 ARP Hardware Security Vulnerability FORK Rootkit 8 10 6 Software Remote
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Table 916 The number of unique attack characteristics of distribution change to the left skewed distribution for each attack Attacks Distributed ARP Hardware Security Vulnerability FORK Rootkit Software Remote
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Summary
95 SUMMARY
This chapter describes the distribution change characteristics of the ten attacks, excluding the FTP Buffer Over ow Attack due to its short attack duration The attack groupings based on the same attack characteristics and the opposite attack characteristics are presented, along with the unique attack characteristics of each attack Although the subtle Hardware Keylogger attack does not manifest any signi cant mean shift characteristics, the probability distribution feature reveals many characteristics of this subtle attack As discussed in 8, monitoring the variables with the unique attack characteristics of each attack can be considered when detecting and identifying that attack However, it may be more ef cient to consider monitoring the variables with the same or opposite characteristics among attacks through a unique combination of those variables for each attack in order to reduce the total number of variables that need to be monitored when detecting and identifying any of these attacks An optimization problem of nding the smallest number of such variables to produce a unique combination for each attack is described in 18 This chapter also reveals the relationships among the ten attacks through the hierarchical clustering of the attacks based on their shared or opposite attack characteristics As discussed in 8, the grouping of the attacks as well as the similarity and difference in data characteristics underlying each attack group is helpful in recognizing the nature of unknown, novel attacks when they show similar attack data characteristics with one or more groups of known attacks, and in guiding the further investigation of these new attacks to reveal their complete attack characteristics The distribution change characteristics can be used not only to distinguish attacks from normal use activities by considering two categories of activities attack and normal use but also to identify any individual activity of interest by considering any activity as an individual category and uncovering its unique combination of distribution change characteristics Identifying not only attack activity but any individual activity of interest has applications that go beyond cyber attack detection The attack characteristics in the probability distribution feature of the data, which are revealed in this chapter in addition to the attack characteristics in the mean feature described in 8, point out the importance of carrying out the feature extraction when discovering the attack characteristics Although the mean shift attack characteristics can readily be observed by plotting the raw data, the attack characteristics in complex or subtle data features (eg, the probability distribution feature) may not be obvious by looking at the raw data The revealed of attack characteristics in such data features will help us gain more knowledge about attacks and build cyber attack detection models with a high level of detection performance by modeling attack data and normal use data accurately according to the revealed data characteristics of attack and normal use activities Part VI gives more details about how to develop attack and normal use data models based on the attack characteristics such as those described in this chapter, and how to use these data models to build cyber attack detection models with a high level of detection performance Extracting subtle data features not only of activity data but also of state and performance data for cyber attack detection also helps prevent an attacker s attempt to disguise attack actions and evade detection by cyber attack detection systems