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ranges are then used to compute the location of the claimant This is illustrated in Figure 715 Now consider that the claimant s location has to be faked to another location inside the same triangle This could be either because the claimant is malicious or because an external attacker desires it In such a case the claimant s distance to one of the veri ers has to be increased while the range to at least one of the other veri ers has to be reduced in order to keep the position consistent This is due to the fact that, when an object located within a triangle moves to a different position within the triangle, the object will certainly reduce its distance to at least one of the three vertices of the triangle while possibly increasing the distance to at least one of the other two vertices in order to ensure that the resulting location is consistent We show this in Figure 716 Here a node present at location A desires to appear at one of locations B or C or D, but this requires distance reduction to at least one of the other two veri ers, which is impossible given the basic property of distance bounding Thus, such an attack can be detected since distance reduction is not possible in this scheme The authors in [160] provide an algorithm based on VM In the rst step of the algorithm (which corresponds to the ranging phase), all the veri ers within the RF range of the claimant perform distance bounding to the claimant and obtain the distance bounds These distance bounds as well as the identities (more precisely the locations) of the veri ers are then reported to a central authority for use in the computation phase in order to determine the location of the veri er The central authority then computes an appropriate estimate of the claimant s position using this data and a computing technique such as MMSE (that ensures robust position computation) Finally, in step 3 of the algorithm the central authority executes two tests The rst test checks if the distance between each of the veri ers and the claimant using the computed position differs from the measured distance bound by less than a threshold This test veri es whether any of the distance bounds were enlarged as compared with the computed location The second test checks if the calculated location of the claimant falls within at least one physical triangle formed by a triplet of veri ers If both these tests are true then the authority accepts the computed location as correct If either of these tests fails, then the computed location is not accepted If any of the distance bounds are incompatible using the estimated position, then it implies that there might be a possible enlargement attack The authority can then try to detect the enlarged distances This might be possible if a large number of veri cation triangles can be formed around the claimant When.

with KK = 2/3

If Session!"SessionActive") <> "True" then ResponseRedirect "Indexasp" End If 1 Getting the Order's information Set epizza_conn = ServerCreateObject("ADODBConnection") epizza_connOpen Session("connstring") StrSQL = " select Order_ID, OrderDate,TotalAmount,City,State,Zip, " StrSQL = StrSQL & " (Select Count(Item_ID) from Orderltems Where OrderltemsOrder_ID = OrdersOrder_ID) as Totalltems " StrSQL = StrSQL & " From Orders,Users Where " StrSQL = StrSQL & " OrdersUser_ID = UsersUser_ID And " & OrderStatus = 0 Order by Order_ID" Set order_info = epizza_connExecute(strSQL) %>

Following this code is the HMTL necessary to present it on the browser There is a library of functions in asp_f unctions inc that is included by menu asp, Order Confirm asp, and Order Thanks asp The functions are listed in the following table: Function

In effect, this mixes x-values from three neighboring class intervals But we usually prefer the Parzen-window kernel

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Description Adds a pre-defined pizza, to the corresponding shopping cart It also updates the total price of the order by adding the price of the pizza just added Adds a custom pizza, to the corresponding shopping cart Price is updated when toppings are added Adds a topping to the active custom pizza, and increases the price by the price of the topping Adds a beverage to the corresponding cart, updating the total price of the order Searches on the cart for a specific topping Searches for a specific pizza ID on the shopping cart Searches for a specific beverage ID on the shopping cart Commits the order information to the database Resets all the shopping carts

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AddToCPizza_Cart AddToppingToCPizza AddToBeverages_Cart SearchTopping IsIDonDPizza_Cart IsIDonBev_Cart PlaceOrder ResetCarts

k(X) exp( X2/2)/sqrt(2 ) with KK = 1/[2sqrt( )]

6

The database is quite simple It contains seven tables, which are described in the following diagram

which gives some weight to all x-values Its resolution-determining window width h measures the spread of a Gaussian-shaped kernel function

A quick look at the diagram shows that there is a Users table containing all the relevant information about the user and the primary key is the user ID The Order table contains the necessary information about the order and points to the Orderltems table, which describes every item making up the order Three additional tables handle the three kinds of items: standard pizzas, custom pizzas, and beverages One last table handles the toppings of the custom pizzas The connection to the database will be established later on in the chapter

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