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The basic function of an intrusion detection system (IDS) is to monitor incoming tra c at a speci c host connected to a network The IDS creates a pro le of normal user tra c and signals an alarm of intrusion for any detected abnormal tra c, ie tra c not forming part of the normal pro le A problem to this solution of pro le creation, is that the normal tra c changes through time Thus, the pro le gets outdated The danger signal used in danger theory inspired the modeling of an adaptable IDS The danger signal can be de ned as a signal generated by the host if any incoming traf c resulted in abnormal CPU usage, memory usage or security attacks The adaptable IDS will only signal an alarm of abnormal tra c if the IDS receives a danger signal from the host If no danger signal is received from the host, the pro le is adapted to accommodate the new detected normal tra c Danger theory inspired AISs applied to intrusion/anomaly detection can be found in [13, 30]

Arti cial immune systems have been successfully applied to many problem domains Some of these domains range from network intrusion and anomaly detection [13, 30, 176, 279, 280, 327, 373, 374, 457, 458, 803, 804] to data classi cation models [692, 890], virus detection [281], concept learning [689], data clustering [184], robotics [431, 892], pattern recognition and data mining [107, 398, 845, 847] The AIS has also been applied to the initialization of feed-forward neural network weights [189], the initialization of centers of a radial basis function neural network [188] and the optimization of multi-modal functions [181, 294] The interested reader is referred to [175, 183, 185] for more information on AIS applications

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We see the world in three dimensions height, width, and depth However, film and video can only record and reproduce height and width Therefore, you must use some specific visual techniques to fool your viewers into thinking that the flat image they are seeing has depth that it resembles three-dimensional reality When shooting objects or people, place your camera at an angle so you see at least two sides of your subject in the viewfinder For example, if you shoot a person head-on, so you see only his or her front, the result is a perfect driver s license portrait

1 With reference to negative selection as described in Section 1921, discuss the consequences of having small values for r Also discuss the consequences for large values 2 A drawback of negative selection is that the training set needs to have a good representation of self patterns Why is this the case

3 How can an AIS be used for classi cation problems where there is more than two classes 4 How does the self stabilizing AIS improve on AINE 5 How does the enhanced arti cial immune network improve on AINE 6 The enhanced arti cial immune network calculates the number of clones generated by an ARB as nc = l sl (a) Why is the number of clones a function of the stimulation level (b) Explain the consequences of large and small values of l 7 For the aiNet model in Algorithm 195, how does network suppression help to control the size of the ARB population 8 Why should an antibody be mutated less the higher the a nity of the antibody to an antigen training pattern, considering the aiNet model 9 Discuss the in uence of di erent values for the danger signal threshold as applied in the adaptive mailbox problem discussed in Section 1952

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To create the next position, add a keyframe (F6) where you want the next position Then follow the same procedure described in Step 3 to create the next position Continue this procedure until you re done

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