Chapter

Innovations in Hybrid Intelligent Systems

Volume 44 of the series Advances in Soft Computing pp 112-119

Data Security Analysis Using Unsupervised Learning and Explanations

  • G. CorralAffiliated withGrup de Recerca en Sistemes Intelligents Enginyeria i Arquitectura La Salle, Universitat Ramon Llull
  • , E. ArmengolAffiliated withIIIA, Artificial Intelligence Research Institute, CSIC, Spanish Council for Scientific Research
  • , A. FornellsAffiliated withGrup de Recerca en Sistemes Intelligents Enginyeria i Arquitectura La Salle, Universitat Ramon Llull
  • , E. GolobardesAffiliated withGrup de Recerca en Sistemes Intelligents Enginyeria i Arquitectura La Salle, Universitat Ramon Llull

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Abstract

Vulnerability assessment is an effective security mechanism to identify vulnerabilities in systems or networks before they are exploited. However manual analysis of network test and vulnerability assessment results is time consuming and demands expertise. This paper presents an improvement of Analia, which is a security system to process results obtained after a vulnerability assessment using artificial intelligence techniques. The system applies unsupervised clustering techniques to discover hidden patterns and extract abnormal device behaviour by clustering devices in groups that share similar vulnerabilities. The proposed improvement consists in extracting a symbolic explanation for each cluster in order to help security analysts to understand the clustering solution using network security lexicon.

Keywords

Network Security Unsupervised Learning Clustering Explanations