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Volumn 5360 LNAI, Issue , 2008, Pages 470-481

Knowledge discovery from honeypot data for monitoring malicious attacks

Author keywords

Botnet; Density based cluster visualisation; Honeypot data; Internet security; Knowledge discovery; Outlier detection

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIONICS; CLUSTERING ALGORITHMS; COMPUTER CRIME; INTERNET; VISUALIZATION;

EID: 58349122750     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89378-3_48     Document Type: Conference Paper
Times cited : (5)

References (13)
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    • The Honeynet Project (ed.): Know Your Enemy: Learning about Security Threats, 2nd edn. Addison Wesley Professional, Reading (May 2004)
    • The Honeynet Project (ed.): Know Your Enemy: Learning about Security Threats, 2nd edn. Addison Wesley Professional, Reading (May 2004)
  • 5
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    • Grizzard, J.B., Sharma, V., Nunnery, C., Kang, B.B., Dagon, D.: Peer-to-peer botnets: Overview and case study. In: HotBots 2007 (April 2007) Paper No. 1
    • Grizzard, J.B., Sharma, V., Nunnery, C., Kang, B.B., Dagon, D.: Peer-to-peer botnets: Overview and case study. In: HotBots 2007 (April 2007) Paper No. 1
  • 6
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    • Nazario, J.: Botnet tracking: Tools, techniques, and lessons learned (2007) (accessed November 14, 2007), http://www.blackhat.com/presentations/bh- dc-07/Nazario/Paper/bh-dc-07-Nazario-WP.pdf
    • Nazario, J.: Botnet tracking: Tools, techniques, and lessons learned (2007) (accessed November 14, 2007), http://www.blackhat.com/presentations/bh- dc-07/Nazario/Paper/bh-dc-07-Nazario-WP.pdf
  • 7
    • 0347172110 scopus 로고    scopus 로고
    • OPTICS: Ordering points to identify the clustering structure
    • Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: SIGMOD 1999, pp. 49-60 (1999)
    • (1999) SIGMOD 1999 , pp. 49-60
    • Ankerst, M.1    Breunig, M.M.2    Kriegel, H.P.3    Sander, J.4
  • 10
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    • Efficient algorithms for mining outliers from large data sets
    • Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: SIGMOD 2000, pp. 427-438 (2000)
    • (2000) SIGMOD 2000 , pp. 427-438
    • Ramaswamy, S.1    Rastogi, R.2    Shim, K.3
  • 11
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    • Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: SIGMOD 2000, pp. 93-104 (2000)
    • Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. In: SIGMOD 2000, pp. 93-104 (2000)
  • 12
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    • A comparative study of anomaly detection schemes in network intrusion detection
    • Lazarevic, A., Ertöz, L., Kumar, V., Ozgur, A., Srivastava, J.: A comparative study of anomaly detection schemes in network intrusion detection. In: SDM 2003 (2003)
    • (2003) SDM
    • Lazarevic, A.1    Ertöz, L.2    Kumar, V.3    Ozgur, A.4    Srivastava, J.5
  • 13
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    • Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: USENIX SSYM 1998, p. 6 (1998)
    • Lee, W., Stolfo, S.J.: Data mining approaches for intrusion detection. In: USENIX SSYM 1998, p. 6 (1998)


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.