Anomaly-based fault detection in Pervasive Computing System

Byoung Uk Kim, Youssif Al-Nashif, Samer Fayssal, Salim A Hariri, Mazin Yousif

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

The increased complexity of hardware and software resources and the asynchronous interaction among components (such as servers, end devices, network, services and software) make fault detection and recovery very challenging. In this paper, we present innovative concepts for fault detection, root cause analysis and self-healing architectures analyzing the duration of pattern transition sequences during an execution window. In this approach, all interactions among components of Pervasive Computing Systems (PCS) are monitored and analyzed. We use three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is captured indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no occurrences of false alarms for a wide range of scenarios.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th International Conference on Pervasive Services, ICPS 2008
Pages147-155
Number of pages9
DOIs
StatePublished - 2008
Event5th International Conference on Pervasive Services, ICPS 2008 - Sorrento, Italy
Duration: Jul 6 2008Jul 10 2008

Other

Other5th International Conference on Pervasive Services, ICPS 2008
CountryItaly
CitySorrento
Period7/6/087/10/08

Fingerprint

Ubiquitous computing
Fault detection
Computer systems
Computer hardware
Feature extraction
Servers
Engines
Hardware
Recovery

Keywords

  • Abnormality detection
  • Faults
  • Interaction analysis
  • Pattern profiling
  • Performance objectives

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Software

Cite this

Kim, B. U., Al-Nashif, Y., Fayssal, S., Hariri, S. A., & Yousif, M. (2008). Anomaly-based fault detection in Pervasive Computing System. In Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008 (pp. 147-155) https://doi.org/10.1145/1387269.1387294

Anomaly-based fault detection in Pervasive Computing System. / Kim, Byoung Uk; Al-Nashif, Youssif; Fayssal, Samer; Hariri, Salim A; Yousif, Mazin.

Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008. 2008. p. 147-155.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, BU, Al-Nashif, Y, Fayssal, S, Hariri, SA & Yousif, M 2008, Anomaly-based fault detection in Pervasive Computing System. in Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008. pp. 147-155, 5th International Conference on Pervasive Services, ICPS 2008, Sorrento, Italy, 7/6/08. https://doi.org/10.1145/1387269.1387294
Kim BU, Al-Nashif Y, Fayssal S, Hariri SA, Yousif M. Anomaly-based fault detection in Pervasive Computing System. In Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008. 2008. p. 147-155 https://doi.org/10.1145/1387269.1387294
Kim, Byoung Uk ; Al-Nashif, Youssif ; Fayssal, Samer ; Hariri, Salim A ; Yousif, Mazin. / Anomaly-based fault detection in Pervasive Computing System. Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008. 2008. pp. 147-155
@inproceedings{d1400b92505143dcbc0b6026df2c0837,
title = "Anomaly-based fault detection in Pervasive Computing System",
abstract = "The increased complexity of hardware and software resources and the asynchronous interaction among components (such as servers, end devices, network, services and software) make fault detection and recovery very challenging. In this paper, we present innovative concepts for fault detection, root cause analysis and self-healing architectures analyzing the duration of pattern transition sequences during an execution window. In this approach, all interactions among components of Pervasive Computing Systems (PCS) are monitored and analyzed. We use three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is captured indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9{\%} with no occurrences of false alarms for a wide range of scenarios.",
keywords = "Abnormality detection, Faults, Interaction analysis, Pattern profiling, Performance objectives",
author = "Kim, {Byoung Uk} and Youssif Al-Nashif and Samer Fayssal and Hariri, {Salim A} and Mazin Yousif",
year = "2008",
doi = "10.1145/1387269.1387294",
language = "English (US)",
isbn = "9781605581354",
pages = "147--155",
booktitle = "Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008",

}

TY - GEN

T1 - Anomaly-based fault detection in Pervasive Computing System

AU - Kim, Byoung Uk

AU - Al-Nashif, Youssif

AU - Fayssal, Samer

AU - Hariri, Salim A

AU - Yousif, Mazin

PY - 2008

Y1 - 2008

N2 - The increased complexity of hardware and software resources and the asynchronous interaction among components (such as servers, end devices, network, services and software) make fault detection and recovery very challenging. In this paper, we present innovative concepts for fault detection, root cause analysis and self-healing architectures analyzing the duration of pattern transition sequences during an execution window. In this approach, all interactions among components of Pervasive Computing Systems (PCS) are monitored and analyzed. We use three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is captured indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no occurrences of false alarms for a wide range of scenarios.

AB - The increased complexity of hardware and software resources and the asynchronous interaction among components (such as servers, end devices, network, services and software) make fault detection and recovery very challenging. In this paper, we present innovative concepts for fault detection, root cause analysis and self-healing architectures analyzing the duration of pattern transition sequences during an execution window. In this approach, all interactions among components of Pervasive Computing Systems (PCS) are monitored and analyzed. We use three-dimensional array of features to capture spatial and temporal variability to be used by an anomaly analysis engine to immediately generate an alert when abnormal behavior pattern is captured indicating some kind of software or hardware failure. The main contributions of this paper include the innovative analysis methodology and feature selection to detect and identify anomalous behavior. Evaluating the effectiveness of this approach to detect faults injected asynchronously shows a detection rate of above 99.9% with no occurrences of false alarms for a wide range of scenarios.

KW - Abnormality detection

KW - Faults

KW - Interaction analysis

KW - Pattern profiling

KW - Performance objectives

UR - http://www.scopus.com/inward/record.url?scp=70249121450&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=70249121450&partnerID=8YFLogxK

U2 - 10.1145/1387269.1387294

DO - 10.1145/1387269.1387294

M3 - Conference contribution

AN - SCOPUS:70249121450

SN - 9781605581354

SP - 147

EP - 155

BT - Proceedings of the 5th International Conference on Pervasive Services, ICPS 2008

ER -