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SRP assumes that there is a bi-directional security association (SA) between nodes that wish to exchange routing messages The two nodes can share a secret key which they use for protecting the routing messages they exchange SRP focuses on the nonmutable elds of the routing messages and uses the keys that the two nodes share for signing the routing messages and hence ensuring that those portions of the messages are not tampered with As will be explained later, signing only the nonmutable elds of the DSR messages is suf cient for ensuring acceptable operation of the routing protocol SRP de nes a header that is added to each routing message of the underlying protocol (eg DSR) When a node initiates the route discovery process it generates a route request message and attaches the SRP header to it The header has the format shown in Figure 46 The type eld de nes the type of message, that is, route request in this case The query identi er is a random 32-bit identi er The query sequence number is a 32-bit sequence number that increases monotonically for each route request generated by the originator for the speci c route destination The sequence number is initialized when the SA between the two nodes is established If the sequence numbers are exhausted then a new SA needs to be established between the two nodes (although this should be very infrequent due to the size of the sequence number) The message authentication code (MAC) is a 96-bit long eld generated by a hash function (eg SHA-1, MD5) The hash function takes as an input the entire IP header, routing message, the SRP extension, and the shared key between the originator and destination of the routing message The mutable elds are excluded from this calculation The mutable elds include any IP-header mutable elds and the list of hops on the route that are added to the route request as it travels towards the destination When intermediate nodes receive the route request they check whether they have seen the message before If they have not, they add their address in the route request message and rebroadcast the message (just as in case of DSR) Otherwise, they drop the message Nodes keep track of the number of route requests their neighbors generate and tend to ignore (or serve at a lower rate) requests from nodes that generate an excessive number of requests This approach helps reduce denial of service attacks by nodes that generate too many routing requests trying to overload other nodes and consume their available resources Once the destination receives the route request message it checks to make sure that the request is valid The sequence number is checked to make sure that the request is not old The destination node then generates the hash of the route request (as computed by the originator) and compares it with the value in the SRP MAC eld included in the route request.



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the item parameters are estimated, ability estimates are used in the item parameter estimation process (which is not the case in classical test theory) Also, when examinees abilities are estimated, item parameter estimates are incorporated in that process (again, this is not the case in classical test theory) Both ability estimates and item statistics are reported on the same scale, so they look different from classical test scores and item statistics Finally, IRT provides a direct way to estimate measurement error at each ability estimate (score level) In classical test theory, it is common to report a single estimate of error, known as the standard error of measurement, and apply that error to all examinees Clearly, such an approach is less satisfactory than producing an error estimate at each ability score level Item and ability parameter invariance are of considerable value to test developers because they create new directions for assessment such as adaptively administered tests and item banking Also, test development becomes easier with new IRT concepts such as item and test information and ef ciency Of course, the feature of invariance will not always be present Item and ability parameter invariance will be obtained when there is (at least) a reasonable t between the chosen IRT model and the test data Not surprisingly then, considerable importance is attached to determining the t of an IRT model to test data (see, for example, Hambleton et al, 1991) Today, there are IRT models to handle nominal, ordinal, and equal-interval educational and psychological data: one-, two-, and three-parameter normal ogive and logistic models; partial credit and graded response models; cognitive component models; a rating scale model; a nominal response model, and many more Multidimensional normal ogive and logistic models are available too There are at least 100 IRT models in the measurement literature, and at least 10 are receiving fairly extensive use (van der Linden & Hambleton, 1997).

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