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dc.contributor.authorWang, Jingen_US
dc.contributor.authorPaschalidis, Ioannis Ch.en_US
dc.date.accessioned2016-09-29T17:32:30Z
dc.date.available2016-09-29T17:32:30Z
dc.date.issued2014-10
dc.identifier.citationJing Wang, Ioannis Ch Paschalidis. 2014. "Botnet detection using social graph analysis." 52nd Annual Allerton Conference on Communication, Control, and Computing,
dc.identifier.otherhttps://arxiv.org/abs/1503.02337
dc.identifier.urihttps://hdl.handle.net/2144/18015
dc.description.abstractWe consider the problem of finding a control policy for a Markov Decision Process (MDP) to maximize the probability of reaching some states while avoiding some other states. This problem is motivated by applications in robotics, where such problems naturally arise when probabilistic models of robot motion are required to satisfy temporal logic task specifications. We transform this problem into a Stochastic Shortest Path (SSP) problem and develop a new approximate dynamic programming algorithm to solve it. This algorithm is of the actor-critic type and uses a least-square temporal difference learning method. It operates on sample paths of the system and optimizes the policy within a pre-specified class parameterized by a parsimonious set of parameters. We show its convergence to a policy corresponding to a stationary point in the parameters' space. Simulation results confirm the effectiveness of the proposed solution.en_US
dc.language.isoen_US
dc.relation.ispartof52nd Annual Allerton Conference on Communication, Control, and Computing
dc.relation.ispartofseriesCommunication, Control, and Computing (Allerton), 2014 52nd Annual Allerton Conference on;
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComputer crimeen_US
dc.subjectNetwork anomaly detectionen_US
dc.subjectSocial networksen_US
dc.subjectCyber-graphsen_US
dc.titleBotnet detection using social graph analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ALLERTON.2014.7028482
pubs.notesoptpages:en_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-group/Boston Universityen_US
pubs.organisational-group/Boston University/College of Engineeringen_US
pubs.organisational-group/Boston University/College of Engineering/Department of Electrical & Computer Engineeringen_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International