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Horizon extender: long-term preservation of data leakage evidence in web traffic

Published: 08 May 2013 Publication History

Abstract

This paper presents Horizon Extender, a system for long-term preservation of data leakage evidence in enterprise networks. In contrast to classical network intrusion detection systems that keep only packet records of suspicious traffic (black-listing), Horizon Extender reduces the total size of captured network traces by filtering out all records that do not reveal potential evidence about leaked data (white-listing). Horizon Extender has been designed to exploit the inherent redundancy and adherence to protocol specification of general Web traffic. We show in a real-life network including more than 1000 active hosts that Horizon Extender is able to reduce the total HTTP volume by 99.8%, or the outgoing volume by 90.9% to 93.9%, while preserving sufficient evidence to recover retrospectively time, end point identity, and content of information leaked over the HTTP communication channel.

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Cited By

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  • (2018)Supervised learning framework for covert channel detection in LTE-AIET Information Security10.1049/iet-ifs.2017.039412:6(534-542)Online publication date: 1-Nov-2018
  • (2015)FloSISProceedings of the 2015 USENIX Conference on Usenix Annual Technical Conference10.5555/2813767.2813800(445-457)Online publication date: 8-Jul-2015
  • (2014)Achieving robustness and capacity gains in covert timing channels2014 IEEE International Conference on Communications (ICC)10.1109/ICC.2014.6883445(969-974)Online publication date: Jun-2014
  • Show More Cited By

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    cover image ACM Conferences
    ASIA CCS '13: Proceedings of the 8th ACM SIGSAC symposium on Information, computer and communications security
    May 2013
    574 pages
    ISBN:9781450317672
    DOI:10.1145/2484313
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 08 May 2013

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    Author Tags

    1. aggregation
    2. data leakage
    3. incident investigation
    4. network forensics
    5. network security

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    ASIA CCS '13 Paper Acceptance Rate 35 of 216 submissions, 16%;
    Overall Acceptance Rate 418 of 2,322 submissions, 18%

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    Cited By

    View all
    • (2018)Supervised learning framework for covert channel detection in LTE-AIET Information Security10.1049/iet-ifs.2017.039412:6(534-542)Online publication date: 1-Nov-2018
    • (2015)FloSISProceedings of the 2015 USENIX Conference on Usenix Annual Technical Conference10.5555/2813767.2813800(445-457)Online publication date: 8-Jul-2015
    • (2014)Achieving robustness and capacity gains in covert timing channels2014 IEEE International Conference on Communications (ICC)10.1109/ICC.2014.6883445(969-974)Online publication date: Jun-2014
    • (2014)Inline Data Integrity Signals for Passive MeasurementTraffic Monitoring and Analysis10.1007/978-3-642-54999-1_2(15-25)Online publication date: 2014

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