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Monitoring for security intrusion using performance signatures

Published: 28 January 2010 Publication History

Abstract

A new approach for detecting security attacks on software systems by monitoring the software system performance signatures is introduced. We present a proposed architecture for security intrusion detection using off-the-shelf security monitoring tools and performance signatures. Our approach relies on the assumption that the performance signature of the well-behaved system can be measured and that the performance signature of several types of attacks can be identified. This assumption has been validated for operations support systems that are used to monitor large infrastructures and receive aggregated traffic that is periodic in nature. Examples of such infrastructures include telecommunications systems, transportation systems and power generation systems. In addition, significant deviation from well-behaved system performance signatures can be used to trigger alerts about new types of security attacks. We used a custom performance benchmark and five types of security attacks to derive performance signatures for the normal mode of operation and the security attack mode of operation. We observed that one of the types of the security attacks went undetected by the off-the-shelf security monitoring tools but was detected by our approach of monitoring performance signatures. We conclude that an architecture for security intrusion detection can be effectively complemented by monitoring of performance signatures.

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cover image ACM Conferences
WOSP/SIPEW '10: Proceedings of the first joint WOSP/SIPEW international conference on Performance engineering
January 2010
294 pages
ISBN:9781605585635
DOI:10.1145/1712605
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: 28 January 2010

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Author Tags

  1. measurement
  2. monitoring
  3. performance signatures
  4. security

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Overall Acceptance Rate 149 of 241 submissions, 62%

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Cited By

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  • (2024)DROPSYS: Detection of ROP attacks using system informationComputers & Security10.1016/j.cose.2024.103813(103813)Online publication date: Mar-2024
  • (2023)AirKeyLogger: Hardwareless Air-Gap Keylogging Attack2023 IEEE 47th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC57700.2023.00089(637-647)Online publication date: Jun-2023
  • (2023)Detecting Anomalies Through Sequential Performance Analysis in Virtualized EnvironmentsIEEE Access10.1109/ACCESS.2023.329364311(70716-70740)Online publication date: 2023
  • (2022)Sequential Performance Analysis of Systems that Age and Rejuvenate2022 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)10.1109/ISSREW55968.2022.00061(146-153)Online publication date: Oct-2022
  • (2022)A survey on outlier explanationsThe VLDB Journal10.1007/s00778-021-00721-131:5(977-1008)Online publication date: 26-Jan-2022
  • (2020)Automated Scalability Assessment in DevOps EnvironmentsCompanion of the ACM/SPEC International Conference on Performance Engineering10.1145/3375555.3384936(10-10)Online publication date: 20-Apr-2020
  • (2020)A Model-Based Approach to Anomaly Detection Trading Detection Time and False Alarm Rate2020 Mediterranean Communication and Computer Networking Conference (MedComNet)10.1109/MedComNet49392.2020.9191549(1-8)Online publication date: Jun-2020
  • (2020)Improving Predictability of User-Affecting Metrics to Support Anomaly Detection in Cloud ServicesIEEE Access10.1109/ACCESS.2020.30285718(198152-198167)Online publication date: 2020
  • (2020)Malware Detection Based on Multi-level and Dynamic Multi-feature Using Ensemble Learning at HypervisorMobile Networks and Applications10.1007/s11036-019-01503-4Online publication date: 8-Jan-2020
  • (2020)Software and System SecuritySystems Benchmarking10.1007/978-3-030-41705-5_18(389-421)Online publication date: 2020
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