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intended destination Note that this attack is possible due to lack of source authentication of the re ected signal Distance bounding technique, which we consider in Section 731, addresses this vulnerability Distance enlargement can be done as earlier by jamming and replaying the signal later A compromised receiver without any other colluding node, however, can only increase the distance by sending the response long after receiving the signal A compromised sender acting alone though can cause both enlargement as well as reduction of the estimated distance In addition, when the underlying technology is IR or visible light, then a problem is the need for a clear path between the sender and the receiver Both these modes are hence sensitive to the topology An attacker can try to take advantage of multipath in order to bias the estimation of distance This can be done by placing a barrier suf ciently close to the transmitter, thereby removing the line of sight signal We next consider the security vulnerabilities of US systems US-based systems are susceptible to the attacks described earlier for both the one-way mode and the two-way mode Additionally, US-based systems also have the following vulnerability This is based on leveraging the property that US-based systems operate with sound signals which are several orders of magnitude slower than RF signals As a result, it would be possible for an attacker to hear the US signal at the sender and send it faster towards a colluding node near the destination by using RF signals The colluding node could then recreate the US signal such that the destination hears this recreated US signal This would result in the nodes appearing closer (distance reduction) as the estimated distance will be less than the actual distance Localization systems can also be designed using the time-of- ight technique and more than one technology For example, if both US and RF signals are used together, then it would be possible to determine the distance between two devices (sender and receiver) without the need for any time synchronization This is because of the large difference in the speed of light (3 108 m/s) and speed of sound (350 m/s) As a result the RF signal is used for synchronization between the transmitter and the receiver by ignoring the time of ight of this signal Thus, in this case the sender sends RF and US signals at nearly the same time and the time at which the RF signal is received is used by the receiver as the reference time We show this in Figure 74 Then the receiver simply measures the difference between the time at which it received the RF and the time at which it received the US signal This time difference multiplied by the speed of sound gives the distance between the two devices Thus, we see that the ultrasound signal is used for ranging This is the approach used in [147] Using more than one technology though makes it possible for the attacker to launch attacks that exploit the difference in propagation speeds to skew the distance estimates 72113 Received Signal Strength Techniques Location systems based on inferring the distance from the signal strength have also been designed The underlying principle here is that signal strength changes as a function of the distance This technique can be sed to design systems that are hardware-constrained (for example unable to deploy nanosecond precision hardware) In this case either theoretical or empirical models are used to translate signal strength measurements into distance estimates Using one approach the distance can be estimated based on the transmitted and received signal strengths at each of the nodes This could be achieved by having either of nodes report on the transmitted or received power levels to the other node The distance can.

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In addition to supporting all of the anticipated image formats, a common image format ideally is easy to display and portable across various hardware platforms Unfortunately, ease of display and portability are somewhat exclusive You do not want a common image format and a separate display format The format in our examples is a compromise, designed to be as easy as possible to use in the Windows environment while at the same time concealing Windows-specific behavior It is capable of storing images using 1, 4, 8, and 24 bits per pixel PNG and PEG are the only formats that store data using other formats The PNG decoder will convert the data to one of the formats above; the JPEG decoder will only read files with 8-bit sample data Datatype Definitions The source code examples in this book use typedefs for integers that are expected to be a specific size Table 14 lists the integer types used They are defined in the file datatypeh, which also defines a function for performing endian conversions of integers The existing definitions are suitable for most 32-bit little-endian systems, so if you are porting the code to another type of system, this is the first file you need to modify Common Image Class B i t m a p I m a g e is the name of the common image format class It is defined in the file bitimageh The general process followed by all of the encoders and decoders in this book for reading and writing images is BitmapImage image ; XYZDecoder decoder ; XYZEncoder encoder ; ifstream input ("INPUTXYZ", ios::binary) ; decoderReadImage (input, image) ; ofstream output ("OUTPUTXYZ", ios::binary) ; encoderWriteImage (output, image) ; where XYZDecoder and XYZEncoder are the decoder and encoder classes for the image type.

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Several researchers have proposed a variety of techniques for detecting routing attacks using anomaly detection For example, Zhang and Lee [104, 105] have discussed the use of anomaly detection for detecting attacks against AODV, dynamic source routing (DSR), and destination-sequenced distance-vector routing (DSDV) and have presented experimental results for the effectiveness of the techniques discussed there The big advantage of anomaly-based detection is its simplicity The various values need to be calculated and compared with the pre-determined thresholds The big disadvantage is that this technique can only be used for detecting a subset of routing attacks It is not possible to create appropriate metrics, especially for subtle attacks where a single node is the target of the attack The other disadvantage of this technique as discussed earlier is the potential generation of false positives 5623 Misuse Detection Vigna et al [5] describe some examples of potential misuse detection schemes for AODV One potential attack described there is when a node drops AODV RREQ messages or RREP messages This type of attack signi cantly disrupts network connectivity by making it more dif cult to propagate route information The detection scheme proposed is based on detectors that monitor the behavior of neighboring nodes If neighbors do not propagate the routing messages as expected within the expected timeout then an attack is inferred Several other attacks that can be detected using this technique are also described there 5624 Speci cation-Based Detection The speci cation-based intrusion detection technique is usually based on building nite state machines that re ect the expected behavior of a node as discussed earlier Tseng et al [4] propose a nite state machine for detecting attacks against AODV using such an approach For example, whenever a node receives a new RREQ it either forwards the RREQ message if it does not know the path to the destination or generates a RREP message If the node does not do either then it is possibly malicious Also, if it generates incorrect RREP message advertising incorrect paths, then that is also is an indication of malicious behavior In order to detect such behavior the detector needs to maintain up-to-date information on the current view of available paths that the monitored node is aware of The previous discussion illustrates the challenges associated with the speci cationbased intrusion detection technique A detector eeds to monitor a node very closely.

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