Detecting Implicit Security Exceptions Using an Improved Variable-Length Sequential Pattern Mining Method

Jinfu Chen, Saihua Cai, Dave Towey, Lili Zhu, Rubing Huang, Hilary Ackah-Arthur, Michael Omari

Research output: Journal PublicationArticlepeer-review


The process of component security testing can produce massive amounts of monitor logs. Current approaches to detect implicit security exceptions (those which cannot be identified by visual inspection alone) compare correct execution sequences with fixed patterns mined from the execution of sequential patterns in the monitor logs. However, this is not efficient and is not suitable for mining large monitor logs. To enable effective mining of implicit security exceptions from large monitor logs, this paper proposes a method based on improved variable-length sequential pattern mining. The proposed method first mines the variable-length sequential patterns from correct execution sequences and from actual execution sequences, thus reducing the number of patterns. The sequential patterns are then detected using the Sunday string-searching algorithm. We conducted an experimental study based on this method, the results of which show that the proposed method can efficiently detect the implicit security exceptions of components.

Original languageEnglish
Pages (from-to)1235-1268
Number of pages34
JournalInternational Journal of Software Engineering and Knowledge Engineering
Issue number8
Publication statusPublished - 1 Oct 2017


  • Component testing
  • implicit security detection
  • monitor logs
  • pattern detecting
  • sequential pattern mining

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence


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