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cost determined at the end of each period prior to the expiration of the service period should be allocated to expense over the service period Changes in the estimated total compensation cost attributable to increases or decreases in the quoted market price of the employer s capital stock subsequent to the expiration of the service period (but prior to the measurement date) should be charged or credited to expense each period as the changes occur If a variable award is granted for past services, estimated total compensation cost determined at the date of grant is charged to expense of the period in which the award is granted Changes in the estimated total compensation cost attributable to increases or decreases in the quoted market price of the employer s capital stock subsequent to the date of grant (but prior to the measurement date) should be charged or credited to expense each period as the changes occur (e) CANCELED OR FORFEITED RIGHTS APB Opinion No 25 states in paragraph 15: If a stock option is not exercised (or awarded stock is returned to the corporation) because an employee fails to ful ll an obligation, the estimate of compensation expense recorded in previous periods should be adjusted by decreasing compensation expense in the period of forfeiture Application of this paragraph to a situation where an award is canceled or forfeited because employment is terminated prior to vesting of the award is straightforward; the previously accrued compensation should be eliminated by decreasing compensation expense in the period of cancellation or forfeiture However, prior to the issuance of FASB Interpretation No 28, the application of this paragraph to combination plans was unclear In a combination plan that permits an employee to elect either a xed award (eg, a stock option) or a variable award (eg, an SAR), FASB Interpretation No 28 speci es that compensation cost should be accrued based on the presumption that the employee will elect the variable award, unless there is evidence to the contrary In cases involving combination plans where the employer has accrued compensation based on the presumption that the employee will elect to exercise the variable award and due to a change in circumstances, it becomes more likely that settlement will be based on the xed award (eg, when appreciation in the quoted market price of the employer s capital stock exceeds the maximum appreciation an employee is entitled to receive upon exercise of an SAR), FASB Interpretation No 28 speci es that the compensation accrued with respect to the variable award should not be adjusted by decreasing compensation expense, but should be recognized as consideration for the stock issued upon settlement of the xed award However, FASB Interpretation No 28 further speci es that, if both the xed award and the variable award are forfeited or canceled, accrued compensation should be eliminated by decreasing compensation expense during the period of forfeiture or cancellation EITF Issue No 87 33, Stock Compensation Issues Related to Market Decline, provides further clari cation of APB Opinion No 25, paragraph 15 In this pronouncement, Task Force members agreed that the reversal of previously measured compensation would be appropriate only if the forfeiture or cancellation of an option or award results from the employee s termination or nonperformance (f) ACCOUNTING FOR INCOME TAXES UNDER APB OPINION NO 25 Compensation expense associated with stock-based compensation awards is often deductible by the employer for income tax purposes in a different period from when such expense is recognized for nancial reporting purposes Such differences are temporary differences and should be accounted for as speci ed in FASB Statement No 109, Accounting for Income Taxes .

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Table 102 gives some examples of the attack characteristics in autocorrelation change In Table 102, A+ indicates an autocorrelation increase attack characteristic, and A indicates an autocorrelation decrease attack characteristic In the Apache Resource DoS attack, the variables, IP\Datagrams Sent/sec, which measures the rate of IP datagrams supplied to IP for transmission, and Network Interface\Packets/sec, which measures the rate of packets sent and received on the network interface, have the autocorrelation decrease characteristic from the high autocorrelation to the low autocorrelation (AH AL in Table 102) Figure 102 plots the data observations of both variables in the attack condition Both variables jump to and stay at a high value in the rst half of the attack and then drop to the value of zero when the server crashes in the second half of the attack A high value in the rst half, zero in the second half and the average value in the middle of this high value and zero produce a low autocorrelation coef cient according to Formula 101 In the ARP Poison attack, the variables, Network Interface\Bytes Received/sec and Network Interface\Packets Received/sec measuring the rate of bytes and packets respectively received on the network interface, have the autocorrelation increase characteristic from the low autocorrelation under the inactive condition to the medium autocorrelation under the attack condition Figure 103 plots the data observations of Network Interface\Packets Received/sec under the attack condition The data plot shows a cyclic pattern at a frequency corresponding to one of the ten lags but not all the ten lags, producing the medium autocorrelation The autocorrelation increase characteristic of Network Interface\Packets Received/sec also appears in the Security Scan attack due to a cyclic pattern of the time series data at a given lag caused by the repetitive network requests to the victim computer from the attacker The variable, Memory\Write Copies/sec, measures the number of page faults which are caused by the memory-write attempts but are satis ed by copying the page from elsewhere in the physical memory, that is, by sharing the data already in the memory This variable shows an autocorrelation decrease from the high autocorrelation under the inactive condition to the medium autocorrelation under the Fork Bomb attack Under the inactive condition, this variable has almost a onstant value with a few exceptions, producing the average value different from the constant value, a large autocorrelation coef cient at all the ten lags, and thus the high autocorrelation However, the time series data under the Fork Bomb attack has a cyclic pattern at a given lag but not all the ten lags due to the repetitive creation of many processes of the same program, producing the medium autocorrelation.

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Making Code 3/9 In .NET Using Barcode maker for ASP . UCC - 14 Printer In Java Using Barcode generator for .same constant value is high correlated with each other, a variable with a onstant value under a given condition is assigned the high autocorrelation level, AH As discussed in 8, although the above procedure focuses on the autocorrelation change attack characteristics, the autocorrelation change characteristics of 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 distinct category for identifying each distinct activity for purposes other than cyber attack detection.Related: Data Matrix Generator .NET , C# Code 128 Generating , Print UPC-A .NET WinForms

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see if the same characteristic (the same variable with the same autocorrelation change) also manifests. Draw DataMatrix In VB.NET Using Barcode maker for Visual .Related: Printing Codabar VB.NET , Print UPC-A VB.NET , Java ISBN Generator

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Code-39 Creation In C# Using Barcode maker for VS .Using Barcode drawer for Visual Studio .NET Control to generate, create barcode image in S .NET applications.Table 108 gives the number of the autocorrelation increase characteristics for each object that are unique to each attack For example, the ARP Poison attack shows a unique autocorrelation increase characteristic in three Cache variables, Cache\Data Maps/sec, Cache\Copy Read Hits %, and Cache\Read Aheads/sec The Apache Resource DoS attack has the unique autocorrelation increase characteristics in the Network Interface and Physical Disk objects only The Software Keylogger attack produces the unique autocorrelation increase characteristics in nine objects All nine attacks have the unique autocorrelation increase characteristics in multiple objects Table 109 gives the number of the autocorrelation decrease characteristics that are unique to each attack For example, the Software Keylogger attack shows a unique autocorrelation decrease characteristic in Cache\Lazy Write Flushes/sec Since an autocorrelation increase characteristic of this variable appears in he Rootkit attack, this variable is also counted as one opposite characteristic between the Software Keylogger and Rootkit attacks in Table 107 The Rootkit attack has the unique autocorrelation decrease characteristics in the Process and Processor objects only The Vulnerability Scan attack has the unique autocorrelation characteristics in the Cache and Process objects only All nine attacks have the unique autocorrelation increase characteristics in multiple objects.Related: Generate EAN-13 C# , Code 39 Generating .NET , Make Interleaved 2 of 5 Java

UCC - 13 In .NET Using Barcode maker for Visual . Recognize Barcode In Java Using Barcode recognizer for .For the collected data of the Windows performance objects described in 7, the same data screening procedure as described in Section 82 is performed to eliminate the data variables that have the observations of the same value under all the three conditions: the inactive, attack and norm conditions Each of the remaining data variables is analyzed to extract the probability distribution feature and discover the distribution change characteristics in the following steps: 1 For the 10-minute data under the inactive condition and the attack data for the entire attack period from Run 1 of the data collection: (a) Apply the test procedure described in Section 92 to the 10-minute data under the inactive condition to identify the probability distribution of the data variable (b) Apply the test procedure to the attack data to identify the probability distribution of the data variable (c) Compare the probability distributions of the variable under the nactive condition and under the attack condition If the probability distributions are different under the two conditions, identify the distribution change as an attack characteristic and denote this distribution change characteristic by the name of the probability distribution under the attack condition For example, DUL indicates that the probability distribution of the data variable changes to the unimodal, left skewed distribution under the attack from a different distribution under the inactive condition 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 distribution change characteristics of the text editing norm.Related: Code 39 Generating Word , ITF-14 Printing Excel , Printing Codabar Java

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