메뉴 건너뛰기




Volumn , Issue , 2004, Pages 16-25

MORPHEUS: Motif oriented representations to purge hostile events from unlabeled sequences

Author keywords

Anomaly detection; Data cleaning; Motifs

Indexed keywords

ALGORITHMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; ONLINE SYSTEMS; SECURITY OF DATA; TREES (MATHEMATICS);

EID: 20444456754     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (10)

References (38)
  • 1
    • 0034832620 scopus 로고    scopus 로고
    • Outlier detection for high dimensional data
    • C. Aggarwal and P. Yu. Outlier Detection for High Dimensional Data. SIGMOD, 2001.
    • (2001) SIGMOD
    • Aggarwal, C.1    Yu, P.2
  • 2
    • 0020849266 scopus 로고
    • Maintaining knowledge about temporal intervals
    • J. Allen. Maintaining knowledge about temporal intervals. Communications of the ACM 26, 11, 832-843, 1983.
    • (1983) Communications of the ACM , vol.26 , Issue.11 , pp. 832-843
    • Allen, J.1
  • 3
    • 20444495731 scopus 로고    scopus 로고
    • Operating system enhancement to prevent the misuse of system calls
    • M. Bernaschi, E. Gabrielli and L.V. Mancini. Operating System Enhancement to Prevent the Misuse of System Calls. ACM CCS, 2001.
    • (2001) ACM CCS
    • Bernaschi, M.1    Gabrielli, E.2    Mancini, L.V.3
  • 4
    • 0039253819 scopus 로고    scopus 로고
    • LOF: Identifying density-based local outliers
    • M. Breunig, H. Kriegel, R. Ng and J. Sander. LOF: Identifying Density-Based Local Outliers. SIGMOD, pp. 93-104, 2000.
    • (2000) SIGMOD , pp. 93-104
    • Breunig, M.1    Kriegel, H.2    Ng, R.3    Sander, J.4
  • 5
    • 20444501420 scopus 로고    scopus 로고
    • Learning rules and clusters for anomaly detection in network traffic
    • V. Kumar, J. Srivastava and A. Lazarevic (editors), Kluwer
    • P. Chan, M. Mahoney and M. Arshad. Learning Rules and Clusters for Anomaly Detection in Network Traffic. Managing Cyber Threats: Issues, Approaches and Challenges, V. Kumar, J. Srivastava and A. Lazarevic (editors), Kluwer, 2003.
    • (2003) Managing Cyber Threats: Issues, Approaches and Challenges
    • Chan, P.1    Mahoney, M.2    Arshad, M.3
  • 6
    • 85149612939 scopus 로고
    • Fast effective rule induction
    • W. Cohen. Fast Effective Rule Induction. ICML, 1995.
    • (1995) ICML
    • Cohen, W.1
  • 9
    • 0141797880 scopus 로고    scopus 로고
    • A geometric framework for unsupervised anomaly detection: Detecting intrusions in unlabeled data
    • D. Barbara and S. Jajodia (editors), Kluwer
    • E. Eskin, A. Arnold, M. Prerau, L. Portnoy and S. Stolfo. A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data. In D. Barbara and S. Jajodia (editors), Applications of Data Mining in Computer Security, Kluwer, 2002.
    • (2002) Applications of Data Mining in Computer Security
    • Eskin, E.1    Arnold, A.2    Prerau, M.3    Portnoy, L.4    Stolfo, S.5
  • 11
    • 85084160308 scopus 로고    scopus 로고
    • A study in using neural networks for anomaly and misuse detection
    • A. Ghosh and A. Schwartzbard. A Study in Using Neural Networks for Anomaly and Misuse Detection. USENK Security Symposium, 1999.
    • (1999) USENK Security Symposium
    • Ghosh, A.1    Schwartzbard, A.2
  • 12
    • 0014854453 scopus 로고
    • The diagram, a method for comparing sequences. Its use with amino acid and nucleotide sequences
    • A.J. Gibbs and G.A. McIntyre. The diagram, a method for comparing sequences. Its use with amino acid and nucleotide sequences. Eur. J. Biochem. 16:1-11, 1970.
    • (1970) Eur. J. Biochem. , vol.16 , pp. 1-11
    • Gibbs, A.J.1    McIntyre, G.A.2
  • 13
    • 44049102761 scopus 로고    scopus 로고
    • Considering both intra-pattern and inter-pattern anomalies in intrusion detection
    • N. Jiang, K. Hua and S. Sheu. Considering Both Intra-pattern and Inter-pattern Anomalies in Intrusion Detection. ICDM, 2002.
    • (2002) ICDM
    • Jiang, N.1    Hua, K.2    Sheu, S.3
  • 14
    • 84949191342 scopus 로고    scopus 로고
    • Temporal signatures for intrusion detection
    • A. Jones and S. Li. Temporal Signatures for Intrusion Detection. ACSAC, 2001.
    • (2001) ACSAC
    • Jones, A.1    Li, S.2
  • 16
    • 0003858566 scopus 로고    scopus 로고
    • Algorithms for mining distance-based outliers in large data sets
    • E. Knorr and R. Ng. Algorithms for Mining Distance-based Outliers in Large Data Sets. VLDB, 1998.
    • (1998) VLDB
    • Knorr, E.1    Ng, R.2
  • 19
    • 20444444362 scopus 로고    scopus 로고
    • A comparative study of anomaly detection schemes in network intrusion detection
    • A. Lazarevic, L. Ertoz, A. Ozgur, J. Srivastava and V. Kumar. A comparative study of anomaly detection schemes in network intrusion detection, SDM, 2003.
    • (2003) SDM
    • Lazarevic, A.1    Ertoz, L.2    Ozgur, A.3    Srivastava, J.4    Kumar, V.5
  • 21
    • 23944513192 scopus 로고    scopus 로고
    • Use of text categorization techniques for intrusion detection
    • Y. Liao and R. Vemuri. Use of Text Categorization Techniques for Intrusion Detection, 11th USENDC Security Symposium, 2002.
    • (2002) 11th USENDC Security Symposium
    • Liao, Y.1    Vemuri, R.2
  • 24
    • 78149297786 scopus 로고    scopus 로고
    • Learning rules for anomaly detection of hostile network traffic
    • M. Mahoney and P. Chan. Learning Rules for Anomaly Detection of Hostile Network Traffic, ICDM, 2003.
    • (2003) ICDM
    • Mahoney, M.1    Chan, P.2
  • 29
    • 0031684427 scopus 로고    scopus 로고
    • Combinatorial pattern discovery in biological sequences
    • I. Rigoutsos and A. Floratos. Combinatorial pattern discovery in biological sequences. Bioinformatics, 14(1):55-67, 1998.
    • (1998) Bioinformatics , vol.14 , Issue.1 , pp. 55-67
    • Rigoutsos, I.1    Floratos, A.2
  • 30
    • 10044227402 scopus 로고    scopus 로고
    • Learning states and rules for time series anomaly detection
    • S. Salvador, P. Chan and J. Brodie. Learning States and Rules for Time Series Anomaly Detection. FLAIRS, 2004.
    • (2004) FLAIRS
    • Salvador, S.1    Chan, P.2    Brodie, J.3
  • 31
    • 1342274727 scopus 로고    scopus 로고
    • A fast automaton-based method for detecting anomalous program behaviors
    • R. Sekar, M. Bendre, D. Dhurjati and P. Bollineni. A Fast Automaton-based Method for Detecting Anomalous Program Behaviors. IEEE S&P, 2001.
    • (2001) IEEE S&P
    • Sekar, R.1    Bendre, M.2    Dhurjati, D.3    Bollineni, P.4
  • 32
    • 20444501819 scopus 로고    scopus 로고
    • "Why 6?" defining the operational limits of slide
    • K. Tan & R. Maxion. "Why 6?" Defining the Operational Limits of slide. IEEE S&P, 2002.
    • (2002) IEEE S&P
    • Tan, K.1    Maxion, R.2
  • 33
    • 33745443149 scopus 로고    scopus 로고
    • Learning rules from system call arguments and sequences for anomaly detection
    • G. Tandon and P. Chan. Learning Rules from System Call Arguments and Sequences for Anomaly Detection. DMSEC, 2003.
    • (2003) DMSEC
    • Tandon, G.1    Chan, P.2
  • 34
    • 0038011184 scopus 로고    scopus 로고
    • Mimicry attacks on host-based intrusion detection systems
    • D. Wagner and P. Soto. Mimicry Attacks on Host-Based Intrusion Detection Systems. ACM CCS, 2002.
    • (2002) ACM CCS
    • Wagner, D.1    Soto, P.2
  • 35
    • 18844368550 scopus 로고    scopus 로고
    • Detecting intrusions using system calls: Alternative data models
    • C. Wartender, S. Forrest and B. Pearlmutter. Detecting Intrusions Using System Calls: Alternative Data Models. IEEE S&P, 1999.
    • (1999) IEEE S&P
    • Wartender, C.1    Forrest, S.2    Pearlmutter, B.3
  • 36
    • 20444507366 scopus 로고    scopus 로고
    • An intrusion-detection system based on the teiresias pattern-discovery algorithm
    • A. Wespi, M. Dacier and H. Debar. An Intrusion-Detection System Based on the Teiresias Pattern-Discovery Algorithm. Proc. EICAR, 1999.
    • (1999) Proc. EICAR
    • Wespi, A.1    Dacier, M.2    Debar, H.3
  • 37
    • 0037636215 scopus 로고    scopus 로고
    • Intrusion detection using variable-length audit trail patterns
    • A. Wespi, M. Dacier and H. Debar. Intrusion detection using variable-length audit trail patterns. RAID, 2000.
    • (2000) RAID
    • Wespi, A.1    Dacier, M.2    Debar, H.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.