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Rating 3.6 Such subtle variations of the attack may be particularly dif cult to detect This is because packets may be dropped as they travel the network not only due to malicious reasons but also for valid reasons For example, when the network gets overloaded, congestion may result in dropping of packets In other cases, the lossy nature of wireless links might result in intermittent packet drops Random dropping of packets by a node can be easily confused for natural losses thus allowing the misbehaving node to evade detection Certain techniques have been proposed for detecting packet dropping attacks The simplest of those approaches relies on the use of promiscuous monitoring It assumes that some nodes in the network monitor their one-hop neighbors using promiscuous monitoring Through their monitoring, these nodes can observe whether other nodes are behaving as expected Statistical detection techniques have to be used to detect malicious packet dropping behavior This is because some amount of packet dropping may not be due to malicious behavior but rather due to normal (nonmalicious) conditions Such conditions include the packet losses due to the vagaries of the wireless links, congestion as well as due to node mobility The limitations of the promiscuous monitoring approach as discussed in Section 552 can also be addressed by such statistical techniques The thresholds used in the statistical techniques, however, would be important If thresholds in the statistical detection scheme are set too low, this technique may result in a large number of false positives If the thresholds are set too high, malicious users may be able to exploit the detection scheme by dropping packets intermittently while ensuring that the thresholds are not crossed, thereby evading detection We now discuss some more elaborate schemes for detecting packet dropping attacks Rao and Kesidis [101] consider the fact that a node may drop a packet not out of maliciousness but because of congestion The congestion happens because more packets arrive at that node than can be stored in its buffers before they are forwarded They de ne a traf c model that can be used for calculating the expected rate of packet dropping due to congestion A node monitors a suspicious node through promiscuous monitoring and can observe whether the rate at which a suspicious node is dropping packets is consistent with the expected drop rate due to congestion as calculated The calculation assumes a Poisson traf c model and a nonoverloaded network The reason for the latter assumption is to ensure that the statistics of the interpacket arrival times do not change from hop to hop In order for the monitoring node to accurately assess the behavior of the suspicious node, it also needs to know the total traf c arriving at the suspicious node The monitoring node achieves that by asking all the neighbors of the suspicious node to provide the current transmission rate towards the suspicious node This scheme is shown to perform reasonably well Unfortunately the applicability of the scheme is very limited due to the assumptions of the traf c model (eg Poisson arrival times, underloaded network) The scheme also suffers from the limitations of the promiscuous monitoring as discussed earlier The limitations of promiscuous monitoring in detecting packet dropping in this and other schemes can be alleviated by requiring multiple nodes to monitor every node Each node can coordinate with direct neighbors of a node exhibiting suspicious behavior to crosscheck on the behavior of the suspicious node Such an approach that compares observation from multiple nodes would make it dif cult if not impossible for a node to. pdf creator software free download windows xp: PDF Printer for Windows 8.1, PDF Converter for Windows 8.1 pdf creator software for windows 7 Software Download - PDF Printer and Converter for Windows 8 ...
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