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A third less widely used technique is called speci cation-based detection We now discuss each of these techniques in detail The misuse detection technique involves analyzing the collected data for speci c behavior patterns known to be consistent with speci c attacks These behavior patterns are called signatures For example, a UDP packet destined to port 0 can crash some machines [79] The signature of a ping-of-death attack is a very large ping packet; the signature of an RPC locator attack is a packet intended for port 135 that contains a command that the system is not expecting; the signature of a Bubonic attack is made from various values, such as a TTL of 255, a TOS eld value of 0 C9, exactly 20 byte payload in the IP datagram, and the fragment ID value with consistent increments of 256 [79] The misuse detection technique is being used widely today by commercial systems because it allows for very precise detection of attacks, thereby resulting in low false alarm rates When a signature for an attack has been identi ed (typically a bit-by-bit precise speci cation of an attack packet), it is straightforward to detect the attack by comparing a packet against the precise attack signature The problem with misuse detection intrusion detection techniques is that these techniques can only detect previously known attacks with wellde ned patterns of malicious behavior Novel attacks that have not been seen before cannot be detected until a new signature for the attack is created The anomaly detection technique involves looking for behavior that is outside the normal expected behavior This is usually done by utilizing statistical techniques that compare the observed behavior against the statistics of the expected normal behavior These techniques often utilize thresholds (such as amount of excessive loading of CPU, counts of traf c of speci c type, etc) that if exceeded indicate an attack Therefore, detectors utilizing this technique require prior training so that the thresholds used for detecting abnormal behavior are set at the appropriate values The advantage of anomaly-based detection is that this technique does not require the existence of precise signatures and can therefore be used for detecting attacks not previously seen The disadvantage of anomaly-based detection is that, due to its statistical nature, it is more prone to false positives depending on how the thresholds are set There is always a trade-off between setting very tight thresholds (close to normal behavior), thus triggering a lot of false positives when users deviate even slightly from expected behavior, and setting very loose thresholds that minimize false positives but allow.



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division, which pays its employees once a week She earned $462 in the past week, and has claimed three withholding allowances Using the wage bracket table in Exhibit 82, we can find the correct wage bracket that contains her pay range (of at least $460 but less than $470), and then shift horizontally across the table from that wage bracket to the column for three withholding allowances, which shows that her total taxes should be $6557

Figure 75 Hop count-based scheme The shaded node determines the hop count from the three anchors and uses this to determine its location





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These leading-edge States are, of course, not alone in their attempts to move testing programs to computer Over the past decade, many occupational and professional, as well as postsecondary-education tests, have done so Examples include the College-Level Examination Program1 (CLEP1), Graduate Record Examinations1 General Test (GRE1), Graduate Management Admission Test1(GMAT1), National Council of State Boards of Nursing NCLEX1 Examination, Test of English as a Foreign Language1 (TOEFL1), and United States Medical Licensing ExaminationTM (USMLETM) The collective experience of these programs is that computer delivery presents considerable challenges (Wainer & Eignor, 2000) Cost The early entrants into computerized testing bore the cost of creating a computer-based test-center infrastructure, electronic tools for writing items, presentation software, and the large item pools needed to support continuous high-stakes testing K-12 education will bene t enormously from the fact that (1) hardware is much cheaper than it was a decade ago, (2) integrated test authoring and delivery software is now readily available, (3) a universal electronic delivery network built with other people s money now exists (the Internet), and (4) because it has broad instructional use, the local-school space, software, hardware, and Internet connections used for testing can be expensed through other budgets Still, the required investment and operational costs will, at least initially, be large relative to paper testing Among other things, these costs will include vendor charges for testing software, central servers to house test content, training, and technical support Some analyses (eg Neuburger, 2001) suggest that the savings from eliminating such paper processes as printing and shipping will eventually outweigh these costs States are, however, saddled with not only budget de cits from recession and the unplanned expense of homeland defense but also the No Child Left Behind requirement to test every child once a year in grades 3 8 in reading and math While the Act provides signi cant funds for states to implement the required assessments, these monies are expected to be considerably less than the actual cost of implementing the law s testing requirements (GAO, 2003) Given the situation, States may nd it hard to justify the extra upfront expenditures associated with computer testing In fact, in some leading-edge states, online assessment activities are already slowing (Borja, 2003) How can the additional costs of online assessment be met Perhaps the least effective approach would be for each state to create it own system, duplicating the efforts of others with similar population characteristics and education needs Instead, consortia, cooperative agreements, or buying pools for obtaining test questions, telecommunications equipment, computer hardware, testing software, and equipment maintenance should be considered.

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An alternative calculation is shown in Exhibit 83, which shows the underlying formulas that were used to derive the wage bracket tax table in Exhibit 82 Under Alternative One in Exhibit 83, we subtract a dollar amount from an employee s base wage that corresponds to the number of withholding allowances taken, then multiply by a base tax rate, and then reduce the tax rate by a fixed amount to arrive at the income tax Please note that these tables do not include the Social Security or Medicare taxes, as was the case in Exhibit 82 Under Alternative 2 in Exhibit 83, we subtract a dollar amount from an employee s base wage that corresponds to the number of withholding allowances taken, then reduce the taxable wage by a fixed amount, and then multiply by a base tax rate to arrive at the income tax Either method results in an identical income tax

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