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PROJECT TITLE :

Magic Train: Design of Measurement Methods against Bandwidth Inflation Attacks - 2018

ABSTRACT:

Bandwidth measurement is vital for several network applications and services, like peer-to-peer networks, video caching and anonymity services. To win a bandwidth-based mostly competition for a few malicious purpose, adversarial Web hosts might falsely announce a larger network bandwidth. Some preliminary solutions are proposed to the present problem. They will either evade the bandwidth inflation by a consensus read (i.e., opportunistic bandwidth measurements) or detect bandwidth frauds via forgeable tricks (i.e., detection through bandwidth's CDF symmetry). However, smart adversaries will simply remove the forgeable tricks and report an equally larger bandwidth to avoid the consensus analyses. To defend against the good bandwidth inflation frauds, we have a tendency to style magic train, a brand new measurement methodology that combines hit and miss packet train with estimated round-trip time (RTT) for detection. The inflation behaviors will be detected through highly contradictory bandwidth results calculated using completely different magic trains or a train's different segments, or large deviation between the estimated RTT and therefore the RTT reported by the train's initial packet. Being an uncooperative measurement method, magic train can be simply deployed on the Web. We have a tendency to have implemented the magic train using RAW socket and LibPcap, and evaluated the implementation in an exceedingly controlled testbed and the.Net. The results have successfully confirmed the effectiveness of magic train in detecting and preventing sensible bandwidth inflation attacks.


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