On off-topic access detection in information systems

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2005, Pages 353-354

  Goharian, Nazli ^a   Ma, Ling ^a  

^a ILLINOIS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Detection; Insider threat; Misuse; Security; Unauthorized access

Indexed keywords

DETECTION; INSIDER THREAT; MISUSE; SECURITY; UNAUTHORIZED ACCESS;

DATA MINING; ERROR DETECTION; FEEDBACK; INFORMATION RETRIEVAL; OPTIMIZATION; PERFORMANCE;

INFORMATION SCIENCE;

EID: 33745772463     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1099554.1099663     Document Type: Conference Paper

References (6)

1. B. Aleman-Meza, et al: An Ontological Approach to the Document Access Problem of Insider Threat. IEEE Intelligence and Security Informatics (ISI), 2005.
2. R. Cathey, et al: Misuse Detection for Information Retrieval Systems. ACM Conference on Information and Knowledge Management (CIKM), 2003.
3. N. Goharian, et al: Detecting Misuse of Information Retrieval Systems Using Data Mining Techniques. IEEE Intelligence and Security Informatics (ISI), 2005.
4. N. Goharian, L. Ma: Query Length Impact on Misuse Detection in Information Retrieval Systems. http://ir.iit.edu/publications/downloads/SAC05- 0040.pdf (version appeared in ACM Symposium on Applied Computing (SAC), 2005).
5. S. Symonenko, et al: Semantic Analysis for Monitoring Insider Threats. IEEE Intelligence and Security Informatics (ISI), 2004.
6. O. Yilmazel, et al: Leveraging One-Class SVM and Semantic Analysis to Detect Anomalous Content. IEEE Intelligence and Security Informatics (ISI), 2005.