Article

Detecting Anomalous and Unknown Intrusions Against Programs

Authors:

A. K. Gosh,

J. Wanken,

F. CharronAuthors Info & Claims

ACSAC '98: Proceedings of the 14th Annual Computer Security Applications Conference

Page 259

Published: 07 December 1998 Publication History

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Shu XYao DRamakrishnan NJaeger T(2017)Long-Span Program Behavior Modeling and Attack DetectionACM Transactions on Privacy and Security10.1145/310576120:4(1-28)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3105761
Meng WLi WKwok L(2017)Towards Effective Trust-Based Packet Filtering in Collaborative Network EnvironmentsIEEE Transactions on Network and Service Management10.1109/TNSM.2017.266489314:1(233-245)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TNSM.2017.2664893
Hosseini Bamakan SWang HShi Y(2017)Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problemKnowledge-Based Systems10.1016/j.knosys.2017.03.012126:C(113-126)Online publication date: 15-Jun-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.03.012
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ACSAC '98: Proceedings of the 14th Annual Computer Security Applications Conference

December 1998

ISBN:0818687894

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IEEE Computer Society

United States

Publication History

Published: 07 December 1998

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Shu XYao DRamakrishnan NJaeger T(2017)Long-Span Program Behavior Modeling and Attack DetectionACM Transactions on Privacy and Security10.1145/310576120:4(1-28)Online publication date: 20-Sep-2017
https://dl.acm.org/doi/10.1145/3105761
Meng WLi WKwok L(2017)Towards Effective Trust-Based Packet Filtering in Collaborative Network EnvironmentsIEEE Transactions on Network and Service Management10.1109/TNSM.2017.266489314:1(233-245)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1109/TNSM.2017.2664893
Hosseini Bamakan SWang HShi Y(2017)Ramp loss K-Support Vector Classification-Regression; a robust and sparse multi-class approach to the intrusion detection problemKnowledge-Based Systems10.1016/j.knosys.2017.03.012126:C(113-126)Online publication date: 15-Jun-2017
https://dl.acm.org/doi/10.1016/j.knosys.2017.03.012
Meng WLi WXiang YChoo K(2017)A bayesian inference-based detection mechanism to defend medical smartphone networks against insider attacksJournal of Network and Computer Applications10.1016/j.jnca.2016.11.01278:C(162-169)Online publication date: 15-Jan-2017
https://dl.acm.org/doi/10.1016/j.jnca.2016.11.012
Li WMeng WKwok LIP H(2017)Enhancing collaborative intrusion detection networks against insider attacks using supervised intrusion sensitivity-based trust management modelJournal of Network and Computer Applications10.1016/j.jnca.2016.09.01477:C(135-145)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.jnca.2016.09.014
Li WMeng WLuo XKwok L(2016)MVPSysComputers and Security10.1016/j.cose.2016.04.00760:C(177-192)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.cose.2016.04.007
Payer M(2016)HexPADSProceedings of the 8th International Symposium on Engineering Secure Software and Systems - Volume 963910.1007/978-3-319-30806-7_9(138-154)Online publication date: 6-Apr-2016
https://dl.acm.org/doi/10.1007/978-3-319-30806-7_9
Islam MKuzu MKantarcioglu MPark JSquicciarini A(2015)A Dynamic Approach to Detect Anomalous Queries on Relational DatabasesProceedings of the 5th ACM Conference on Data and Application Security and Privacy10.1145/2699026.2699120(245-252)Online publication date: 2-Mar-2015
https://dl.acm.org/doi/10.1145/2699026.2699120
Costa KPereira LNakamura RPereira CPapa JXavier Falcão A(2015)A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networksInformation Sciences: an International Journal10.1016/j.ins.2014.09.025294:C(95-108)Online publication date: 10-Feb-2015
https://dl.acm.org/doi/10.1016/j.ins.2014.09.025
Meng WLi WKwok L(2015)Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detectionSecurity and Communication Networks10.1002/sec.13078:18(3883-3895)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1002/sec.1307
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Detecting botnet by anomalous traffic

Using artificial anomalies to detect unknown and known network intrusions

Using Artificial Anomalies to Detect Unknown and Known Network Intrusions

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