Machine learning approach to detect intruders in database based on hexplet data structure

Clicks: 196
ID: 88164
2016
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Abstract
Most of valuable information resources for any organization are stored in the database; it is a serious subject to protect this information against intruders. However, conventional security mechanisms are not designed to detect anomalous actions of database users. An intrusion detection system (IDS), delivers an extra layer of security that cannot be guaranteed by built-in security tools, is the ideal solution to defend databases from intruders. This paper suggests an anomaly detection approach that summarizes the raw transactional SQL queries into a compact data structure called hexplet, which can model normal database access behavior (abstract the user's profile) and recognize impostors specifically tailored for role-based access control (RBAC) database system. This hexplet lets us to preserve the correlation among SQL statements in the same transaction by exploiting the information in the transaction-log entry with the aim to improve detection accuracy specially those inside the organization and behave strange behavior. The model utilizes naive Bayes classifier (NBC) as the simplest supervised learning technique for creating profiles and evaluating the legitimacy of a transaction. Experimental results show the performance of the proposed model in the term of detection rate.
Reference Key
darwish2016machinejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Darwish, Saad M.;
Journal journal of electrical systems and information technology
Year 2016
DOI
DOI not found
URL
http://www.sciencedirect.com/science/article/pii/S2314717216300241
Keywords
Education Technology (General) Chemical technology Information technology Technology Electrical engineering. Electronics. Nuclear engineering Science (General) electronic computers. computer science

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