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Result-Based Detection of Insider Threats to Relational databases 19

Result-Based Detection of Insider Threats to Relational Data...

引用

9th ACM Conference on data and Application security and Privacy (CODASPY)

作者： Sallam, Asmaa Bertino, Elisa Purdue Univ Dept Comp Sci W Lafayette IN 47906 USA

ISBN: (纸本)9781450360999

Insiders misuse of resources is a real threat to organizations. According to recent security reports, data has been the most vulnerable to attacks by insiders, especially data located in databases and corporate file servers. Although anomaly detection is an effective technique for flagging early signs of insider attacks, modern techniques for the detection of anomalies in database access are not able to detect several sophisticated data misuse scenarios such as attempts to track data updates and the aggregation of data by an insider that exceeds his/her need to perform job functions. In such scenarios, if the insider does not have prior knowledge of the distribution of the target data, many of his/her queries may extract no data or small amounts of data. Therefore, monitoring the total size of data retrieved by each user and comparing it to normal levels will either result in low anomaly detection accuracy or long time to anomaly detection. In this paper, we propose anomaly detection techniques designed to detect data aggregation and attempts to track data updates. Our techniques infer the normal rates of tables references and tuples retrievals from past database access logs. User queries are then analyzed to detect queries that lead to exceeding the normal data access rates. We evaluated the proposed techniques on the query logs of a real database. The results of the evaluation indicate that when the system configuration parameters are adequately selected and sufficient data is available for training, our techniques have low false alarm generation rate and high anomaly detection accuracy.

关键词： Insider Threats data analytics for security Relational databases Anomaly Detection Temporal Attacks

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Detection of Temporal data Ex-filtration Threats to Relational databases 4

Detection of Temporal Data Ex-filtration Threats to Relation...

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4th IEEE International Conference on Collaboration and Internet Computing (CIC)

作者： Sallam, Asmaa Bertino, Elisa Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781538695029

According to recent reports, the most common insider threats to systems are unauthorized access to or use of corporate information and exposure of sensitive data. While anomaly detection techniques have proved to be effective in the detection of early signs of data theft, these techniques are not able to detect sophisticated data misuse scenarios in which malicious insiders seek to aggregate knowledge by executing and combining the results of several queries. We thus need techniques that are able to track users' actions across time to detect correlated ones that collectively flag anomalies. In this paper, we propose such techniques for the detection of anomalous accesses to relational databases. Our approach is to monitor users' queries, sequences of queries and sessions of database connection to detect queries that retrieve amounts of data larger than the normal. Our evaluation of the proposed techniques indicates that they are very effective in the detection of anomalies.

关键词： Insider Threats data analytics for security Relational databases Anomaly Detection Temporal Attacks

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Detection of Temporal Insider Threats to Relational databases 3

Detection of Temporal Insider Threats to Relational Database...

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3rd IEEE International Conference on Collaboration and Internet Computing (CIC)

作者： Sallam, Asmaa Bertino, Elisa Purdue Univ Dept Comp Sci W Lafayette IN 47907 USA

ISBN: (纸本)9781538625651

The mitigation of insider threats against databases is a challenging problem as insiders often have legitimate access privileges to sensitive data. Therefore, conventional security mechanisms, such as authentication and access control, may be insufficient for the protection of databases against insider threats and need to be complemented with techniques that support real-time detection of access anomalies. The existing real-time anomaly detection techniques consider anomalies in references to the database entities and the amounts of accessed data. However, they are unable to track the access frequencies. According to recent security reports, an increase in the access frequency by an insider is an indicator of a potential data misuse and may be the result of malicious intents for stealing or corrupting the data. In this paper, we propose techniques for tracking users' access frequencies and detecting anomalous related activities in real-time. We present detailed algorithms for constructing accurate profiles that describe the access patterns of the database users and for matching subsequent accesses by these users to the profiles. Our methods report and log mismatches as anomalies that may need further investigation. We evaluated our techniques on the OLTP-Benchmark. The results of the evaluation indicate that our techniques are very effective in the detection of anomalies.

关键词： Insider Threats data analytics for security Relational databases Anomaly Detection Temporal Attacks Time Series Periodicity

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