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Singh et al., 2020 - Google Patents

Detecting intrusive transactions in databases using partially-ordered sequential rule mining and fractional-distance based anomaly detection

Singh et al., 2020

Document ID
17860040007080831947
Author
Singh I
Manuja M
Mathur R
Goswami M
Publication year
Publication venue
International Journal of Intelligent Engineering Informatics

External Links

Snippet

Illegitimate access to databases may compromise their integrity and confidentiality, resulting in legal and financial ramifications for organisations. We propose a database intrusion detection system (DIDS) called fractional distance based anomaly detection with partially …
Continue reading at www.inderscienceonline.com (other versions)

Classifications

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    • G06F21/62Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245Protecting personal data, e.g. for financial or medical purposes
    • G06F21/6263Protecting personal data, e.g. for financial or medical purposes during internet communication, e.g. revealing personal data from cookies
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    • G06F21/6254Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
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    • G06F21/6227Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database where protection concerns the structure of data, e.g. records, types, queries
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    • G06F21/6209Protecting access to data via a platform, e.g. using keys or access control rules to a single file or object, e.g. in a secure envelope, encrypted and accessed using a key, or with access control rules appended to the object itself
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