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Constructor CCommand(CClientSocket* aSocket) aSocket the owning client socket. Protected Member Methods virtual void SendResponse () This method sends the header followed by the rest of the message. It is the normal means of sending a response to the client. virtual void SendCompleteL() This method is called when a send operation has completed. No error is reported by the send operation, because it is always assumed to succeed. Should it fail, the socket is closed and therefore SocketClosing() is called. Observers don t need to take any action on write failure they simply cease to exist. TBool NewMessageReceived (MMessage* aMessage) This method is called by the client socket when a new message is received to allow a persistent command to take ownership. The default version returns EFalse. This should be overridden only by those commands that receive the client request in more than one message. aMessage this argument is not used. returns ETrue if the command has taken ownership of the message, EFalse otherwise. virtual void SocketClosing () This method is called when the socket is closing. void TakeOwnershipOfMessage (MMessage* aMessage) This method is called when the command is created to take ownership of the message. inline CClientSocket* Socket () const This method returns a reference to the owning CClientSocket. inline CMessage*& Message () inline const CMessage* const & Message() const These methods provide access to the CMessage owned by the CCommand.

One of the AIS models based on the classical view of the natural immune system is the model introduced by Forrest et al [281] In this classical AIS, Forrest et al introduced a training technique known as negative selection In the model, all patterns and ALCs are represented by nominal-valued attributes or as binary strings The a nity between an ALC and a pattern is measured using the r-continuous matching rule Figure 191 illustrates the r-continuous matching rule between an ALC and a pattern The r-continuous matching rule is a partial matching rule, ie an ALC detects a pattern if there are r-continuous or more matches in the corresponding positions r is the degree of a nity for an ALC to detect a pattern In Figure 191 there are seven continuous matches between the ALC and the pattern Thus, if r = 4, the ALC matches the pattern in Figure 191, since 7 > r If r > 7, the ALC does not match the pattern in the gure A higher value of r indicates a stronger a nity between an ALC and a pattern A set of ALCs in the model represents the mature T-Cells in the natural immune system A training set of self patterns is used to train the set of ALCs using the

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192 Classical View Models negative selection technique Algorithm 192 summarizes negative selection Algorithm 192 Training ALCs with Negative Selection Set counter na as the number of self-tolerant ALCs to train; Create an empty set of self-tolerant ALCs as C; Determine the training set of self patterns as DT ; while size of C not equal to na do Randomly generate an ALC, xi ; Matched=false; for each self pattern zp DT do if affinity between xi and zp is higher than affinity threshold r then matched=true; break; end end if not matched then Add xi to set C; end end

For each randomly generated ALC, the a nity between the ALC and each self pattern in the training set is calculated If the a nity between any self pattern and an ALC is higher than the a nity threshold, r, the ALC is discarded and a new ALC is randomly generated The new ALC also needs to be measured against the training set of self patterns If the a nity between all the self patterns and an ALC is lower than the a nity threshold, r, the ALC is added to the self-tolerant set of ALCs Thus, the set of ALCs is negatively selected, ie only those ALCs with a calculated a nity less than the a nity threshold, r, will be included in the set of ALCs The trained, self-tolerant set of ALCs is then presented with a testing set of self and non-self patterns for classi cation The a nity between each training pattern and the set of self-tolerant ALCs is calculated If the calculated a nity is below the a nity threshold, r, the pattern is classi ed as a self pattern; otherwise, the pattern is classi ed as a non-self pattern The training set is monitored by continually testing the ALC set against the training set for changes A drawback of the negative selection model is that the training set needs to have a good representation of self patterns Another drawback is the exhaustive replacement of an ALC during the monitoring of the training set until the randomly generated ALC is self-tolerant

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