Autocorrelation change attack characteristics in Visual Studio .NET

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Autocorrelation change attack characteristics
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Line Plot (VeryPureApache1169v*211c)
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\\ALPHA02-VICTIM\Network Interface(Intel[R] PRO_1000 MT Network Connection - Packet Scheduler Miniport)\Packets/sec Line Plot (VeryPureApache1169v*211c) 14000
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4500 4000 \\ALPHA02-VICTIM\IP\Datagrams Sent/sec 3500 3000 2500 2000 1500 1000 500 0 500 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211
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2000 1 16 31 46 61 76 91 106 121 136 151 166 181 196 211
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(a) IP/Datagrams Sent/sec
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(b) Network Interface/Packets/sec
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Figure 102 The data plots of the variables under the Apache Resource DoS attack
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From the above examples, the following time series data patterns producing the low, medium and high levels are observed:
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r Time series data with mostly a constant value but a few exceptions from this constant value
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produces a high autocorrelation level One special case is time series data with a cyclic or seasonal pattern at a lag greater than 10, which produces a high autocorrelation level Another special case is time series data with one constant value for one period and a different constant value for another period, producing the average value different from both constant values not all the ten lags produces a medium autocorrelation level Obviously, time series data with completely independent data observations produces the autocorrelation coef cient of zero and thus the low autocorrelation level according to Formula 101
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Line Plot (pureEttercapOnly1_17 1109v*732c) 90 \\ALPHA02-VICTIM\Network Interface(Intel[R] PRO_1000 MT Network Connection - Packet Scheduler Miniport)\Packets Received/sec 80 70 60 50 40 30 20 10 0 10 1 51 101 151 201 251 301 351 401 451 501 551 601 651 701
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r Time series data with a cyclic or seasonal pattern at one or more lags between 1 and 10 but
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Figure 103 The data plot of Network Interface\Packets Received/sec in the ARP Poison attack
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Autocorrelation change characteristics
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1033 Autocorrelation change attack characteristics by attacks and Windows performance objects
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Tables 103 and 104 present the number of autocorrelation increase characteristics and autocorrelation decrease characteristics, respectively, from the inactive condition to each attack condition for each Windows performance object Table 105 summarizes the ndings from Tables 103 and 104 with a comparison of the autocorrelation increase and autocorrelation decrease attack characteristics Finding 1 in Table 105 indicates that in total 15 objects demonstrate the autocorrelation increase characteristics from the inactive condition to the nine attack conditions and it is total 15 objects demonstrate the autocorrelation decrease characteristics from the inactive condition to the nine attack condition In Finding 2 of Table 105, the objects, ICMP, Redirector and TCP, show only the autocorrelation increase characteristic with no autocorrelation decrease characteristic in various attacks, whereas the objects, Paging File, Server and Server Work Queues, show only the autocorrelation decrease characteristics under various attacks ICMP is affected by only the ARP Poison, Rootkit and Security Audit attacks (see Finding 7 in Table 105) Redirector is affected by the Security Audit attack only (see Finding 4 in Table 105) TCP is affected by the Rootkit attack only (see Finding 4 in Table 105) Paging File is affected by the Software Keylogger attack only (see Finding 4 in Table 105) Server Work Queues is affected by the Security Audit attack only (see Finding 4 in Table 105) Server is affected by the Remote Dictionary and Security Audit attacks (see Finding 11 in Table 105) All other objects have both autocorrelation increase and autocorrelation decrease characteristics Finding 3 indicates that both autocorrelation increase and autocorrelation decrease characteristics of the Process object occur in most of the attacks since each attack introduces its special process(es) The Hardware Keylogger attack produces the largest number of autocorrelation increase and autocorrelation decrease characteristics (see Finding 5 in Table 105) in the Process object The Rootkit attack also introduces a large number of autocorrelation increase characteristics in the Process object (see Finding 5 in Table 105) The Rootkit and Software Keylogger attacks introduce the autocorrelation increase characteristics to a large number of objects, while the ARP Poison attack introduces the autocorrelation decrease characteristics to a large number of objects (see Finding 6 in Table 105) Findings 7 11 indicate a few objects that affect the IP, ICMP, Memory, Objects, and Server objects Note that the mean feature described in 8 does not reveal any attack characteristic for the subtle Hardware Keylogger attack However, the autocorrelation feature described in this chapter reveals a large number of attack characteristics for this attack, including the autocorrelation increase characteristics in 7 objects and the autocorrelation decrease characteristics in 6 objects Among all the nine attacks, the Hardware Keylogger attack also causes the largest number of both autocorrelation increase and autocorrelation decrease characteristics in the Process object
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