A Statistical Anomaly-Based Algorithm for On-line Fault Detection in Complex Software Critical Systems

  • Antonio Bovenzi
  • Francesco Brancati
  • Stefano Russo
  • Andrea Bondavalli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6894)


The next generation of software systems in Large-scale Complex Critical Infrastructures (LCCIs) requires efficient runtime management and reconfiguration strategies, and the ability to take decisions on the basis of current and past behavior of the system. In this paper we propose an anomalybased approach for the detection of online faults, which is able to (i) cope with highly variable and non-stationary environment and to (ii) work without any initial training phase. The novel algorithm is based on Statistical Predictor and Safety Margin (SPS), which was initially developed to estimate the uncertainty in time synchronization mechanisms.

The SPS anomaly detection algorithm has been experimented on a case study from the Air Traffic Management (ATM) domain. Results have been compared with an algorithm, which adopts static thresholds, in the same scenarios [5]. Experimental results show limitations of static thresholds in highly variable scenarios, and the ability of SPS to fulfill the expectations.


Anomaly detection SPS on-line software fault diagnosis 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Antonio Bovenzi
    • 1
  • Francesco Brancati
    • 2
  • Stefano Russo
    • 1
  • Andrea Bondavalli
    • 2
  1. 1.Dipartimento di Informatica e Sistemistica (DIS)Università degli Studi di Napoli “Federico II”NapoliItaly
  2. 2.Dipartimento di Sistemi e Informatica (DSI)Università degli Studi di FirenzeItaly

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