TY - GEN
T1 - Neighbor contamination to achieve complete bottleneck control
AU - Schweitzer, Nadav
AU - Stulman, Ariel
AU - Shabtai, Asaf
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/11/13
Y1 - 2016/11/13
N2 - Black-holes, gray-holes and, wormholes, are devastating to the correct operation of any network. These attacks (among others) are based on the premise that packets will travel through compromised nodes, and methods exist to coax routing into these traps. Detection of these attacks are mainly centered around finding the subversion in action. In networks, bottleneck nodes-those that sit on many potential routes between sender and receiver-are an optimal location for compromise. Finding naturally occurring path bottlenecks, however, does not entitle network subversion, and as such are more difficult to detect. The dynamic nature of mobile ad-hoc networks (MANETs) causes ubiquitous routing algorithms to be even more susceptible to this class of attacks. Finding perceived bottlenecks in an OLSR based MANET, is able to capture between 50%-75% of data. In this paper we propose a method of subtly expanding perceived bottlenecks into complete bottlenecks, raising capture rate up to 99%; albeit, at high cost. We further tune the method to reduce cost, and measure the corresponding capture rate.
AB - Black-holes, gray-holes and, wormholes, are devastating to the correct operation of any network. These attacks (among others) are based on the premise that packets will travel through compromised nodes, and methods exist to coax routing into these traps. Detection of these attacks are mainly centered around finding the subversion in action. In networks, bottleneck nodes-those that sit on many potential routes between sender and receiver-are an optimal location for compromise. Finding naturally occurring path bottlenecks, however, does not entitle network subversion, and as such are more difficult to detect. The dynamic nature of mobile ad-hoc networks (MANETs) causes ubiquitous routing algorithms to be even more susceptible to this class of attacks. Finding perceived bottlenecks in an OLSR based MANET, is able to capture between 50%-75% of data. In this paper we propose a method of subtly expanding perceived bottlenecks into complete bottlenecks, raising capture rate up to 99%; albeit, at high cost. We further tune the method to reduce cost, and measure the corresponding capture rate.
KW - Internet of things
KW - Manet
KW - Mobile
KW - Network bottlenecks
KW - Wormhole
UR - http://www.scopus.com/inward/record.url?scp=85006999991&partnerID=8YFLogxK
U2 - 10.1145/2988287.2989146
DO - 10.1145/2988287.2989146
M3 - Conference contribution
AN - SCOPUS:85006999991
T3 - MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
SP - 247
EP - 253
BT - MSWiM 2016 - Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems
PB - Association for Computing Machinery, Inc
T2 - 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems, MSWiM 2016
Y2 - 13 November 2016 through 17 November 2016
ER -