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nodes that they call insider and outsider nodes The insider nodes are assumed to be trusted and are the ones which execute the IDS Thus all the explanation above applies to insider nodes The authors had to consider these types of special ad hoc networks since the proposed IDS placement algorithms (such as random placement, GO-DOM, approximation to optimal) are not secure to attacks from untrusted nodes Rather the security of these algorithms is derived from the fact that the nodes executing these algorithms are trusted The same ideas can be applied to general ad hoc networks provided secure versions of the IDS placement algorithms can be designed This was the reason why we have described the algorithms more generally here An important assumption in [80, 83] is that the nodes that execute IDS in the promiscuous mode operate perfectly, but this is not always true A node may fail to capture and analyze the packets perfectly due to many reasons This failure may happen due to faults in the node, power-saving operations that the node might use or if the attack has been designed to evade the IDS, or if the attacker successfully launches a DoS attack on the node, or if the node does not receive the packet due to the vagaries of promiscuous mode monitoring such as collisions (see Section 552), poor transmission quality in wireless links, and so on Such a failure model is considered in [81], where the authors extend the algorithms proposed in [80, 83] The authors, however, assume that a node is not compromised and hence never reports falsely on the occurrence of malicious packets To address such failure, the authors propose to make use of redundancy whereby more than one node in a neighborhood monitors packets promiscuously We show this in Figure 54 In this gure the shaded nodes indicate the nodes that are executing IDS We see that node A is protected by four other nodes that execute IDS As a result, traf c emanating from node A will always be analyzed if at least three of the four nodes (C, D, E, and F) have not failed at any point in time A question is related to the number of nodes in a neighborhood, denoted as k, that have to execute IDS to reach the desirable level of detection The authors address this by relating the number k to the desired detection probability Using this number k, the authors provide extensions to the mathematical models used in the previous techniques to ensure that every node is covered by k other nodes that execute IDS In addition, the approximation algorithms are also extended by considering a node as satis ed if it is covered by more than k nodes In each iteration, the approximation algorithms then select the node with the maximum number of unsatis ed neighbors to execute the IDS.



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High and Low Stakes There are two ways of looking at testing from the test taker s point of view It can be seen as a barrier to some goal (such as a job, or certi cation), which he or she has to overcome in order to progress his or her career as desired; or it can be seen as an opportunity for discovering more about oneself Typically we regard the former as high stakes and the latter as low stakes This classi cation re ects more what is done with the information gained from the test than the risks associated with the testing process For so-called high stake testing, the test scores are provided for someone other than the test taker to make a decision that can have a signi cant impact not only on the test taker but also on others (the hiring organisation, potential clients of licensed practitioners etc) Assessment for self-discovery purposes, on the other hand, though it can have a very high impact on the individual concerned, tends to have less impact on others In developing models of impact, we need to consider a larger number of factors than are generally considered in the high versus low stake distinction 1 What is the impact of the results of the test on the person who sponsored the assessment (eg the third party who is using test data to aid in the making of a decision) 2 What is the collateral impact of the results of the test on people associated with the test taker (eg family and friends) 3 What is the direct impact of the results of the test on the test taker In each case, these impacts might be moderated by the following 1 2 3 4 Cheating or collusion by the test taker The use of inappropriate tests or tests with poor psychometric properties Inappropriate modes of administration Poor reporting.

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Score Reporting One of the major factors affecting the impact a test has is score reporting How the information about test performance is presented and to whom are the main determiners of impact It is surprising, therefore, that score reporting has

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