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Please use this identifier to cite or link to this item: https://dspace.iiti.ac.in/handle/123456789/4574
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dc.contributor.authorHubballi, Neminathen_US
dc.contributor.authorTiwari, Namrataen_US
dc.contributor.authorKhandait, Pratibhaen_US
dc.date.accessioned2022-03-17T01:00:00Z-
dc.date.accessioned2022-03-17T15:34:52Z-
dc.date.available2022-03-17T01:00:00Z-
dc.date.available2022-03-17T15:34:52Z-
dc.date.issued2020-
dc.identifier.citationHubballi, N., Tiwari, N., & Khandait, P. (2020). POSTER: Distributed SSH bruteforce attack detection with flow content similarity and login failure reputation. Paper presented at the Proceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020, 916-918. doi:10.1145/3320269.3405443en_US
dc.identifier.isbn9781450367509-
dc.identifier.otherEID(2-s2.0-85096400002)-
dc.identifier.urihttps://doi.org/10.1145/3320269.3405443-
dc.identifier.urihttps://dspace.iiti.ac.in/handle/123456789/4574-
dc.description.abstractIn this paper we propose a method to detect distributed bruteforcing by modeling failed login attempts as a Poisson probability distribution. We use content similarity between known SSH connection and flow characteristics of failed login attempts to attribute a flow to SSH application and subsequently either as failure or success. Using the failed login count in a window time, we label window as either normal or containing bruteforce attempts. © 2020 Owner/Author.en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machinery, Incen_US
dc.sourceProceedings of the 15th ACM Asia Conference on Computer and Communications Security, ASIA CCS 2020en_US
dc.subjectComputer scienceen_US
dc.subjectComputersen_US
dc.subjectBrute-force attacken_US
dc.subjectContent similarityen_US
dc.subjectFlow charac-teristicsen_US
dc.subjectSSH connectionsen_US
dc.subjectPoisson distributionen_US
dc.titlePOSTER: Distributed SSH Bruteforce Attack Detection with Flow Content Similarity and Login Failure Reputationen_US
dc.typeConference Paperen_US
Appears in Collections:Department of Computer Science and Engineering

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