Detecting Implicit Security Exceptions Using an Improved Variable-Length Sequential Pattern Mining Method
Autor: | Lili Zhu, Saihua Cai, Michael Omari, Rubing Huang, Dave Towey, Jinfu Chen, Hilary Ackah-Arthur |
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Rok vydání: | 2017 |
Předmět: |
Unit testing
Computer Networks and Communications Computer science business.industry Process (computing) Pattern recognition 0102 computer and information sciences 02 engineering and technology Variable length computer.software_genre 01 natural sciences Computer Graphics and Computer-Aided Design Security testing 010201 computation theory & mathematics Artificial Intelligence Component (UML) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Data mining Artificial intelligence Sequential Pattern Mining business computer Software |
Zdroj: | International Journal of Software Engineering and Knowledge Engineering. 27:1235-1268 |
ISSN: | 1793-6403 0218-1940 |
DOI: | 10.1142/s0218194017500462 |
Popis: | 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. |
Databáze: | OpenAIRE |
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