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Volumn , Issue , 2014, Pages 39-50

Detection of stealthy malware activities with traffic causality and scalable triggering relation discovery

Author keywords

Anomaly detection; Network security; Stealthy malware

Indexed keywords

COMPUTER CRIME; MALWARE;

EID: 84984908573     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2590296.2590309     Document Type: Conference Paper
Times cited : (51)

References (44)
  • 4
    • 79960360109 scopus 로고    scopus 로고
    • Malware analysis with tree automata inference
    • Springer
    • D. Babić, D. Reynaud, and D. Song. Malware analysis with tree automata inference. In Computer Aided Verification, pages 116-131. Springer, 2011.
    • (2011) Computer Aided Verification , pp. 116-131
    • Babić, D.1    Reynaud, D.2    Song, D.3
  • 9
    • 85076921594 scopus 로고    scopus 로고
    • Automating network application dependency discovery: Experiences, limitations, and new solutions
    • USENIX Association
    • X. Chen, M. Zhang, Z. M. Mao, and P. Bahl. Automating network application dependency discovery: Experiences, limitations, and new solutions. In Proceedings of OSDI, pages 117-130, 2008. USENIX Association.
    • (2008) Proceedings of OSDI , pp. 117-130
    • Chen, X.1    Zhang, M.2    Mao, Z.M.3    Bahl, P.4
  • 11
    • 58049219641 scopus 로고    scopus 로고
    • Mining specifications of malicious behavior
    • M. Christodorescu, S. Jha, and C. Kruegel. Mining specifications of malicious behavior. In ISEC, pages 5-14, 2008.
    • (2008) ISEC , pp. 5-14
    • Christodorescu, M.1    Jha, S.2    Kruegel, C.3
  • 12
    • 34249753618 scopus 로고
    • Support-vector networks
    • C. Cortes and V. Vapnik. Support-vector networks. Machine learning, 20(3):273-297, 1995.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 16
  • 18
    • 85075837457 scopus 로고    scopus 로고
    • BotMiner: Clustering analysis of network traffic for protocol-and structure-independent botnet detection
    • G. Gu, R. Perdisci, J. Zhang, and W. Lee. BotMiner: Clustering analysis of network traffic for protocol-and structure-independent botnet detection. In Proceedings of the 17th USENIX Security Symposium, 2008.
    • (2008) Proceedings of the 17th USENIX Security Symposium
    • Gu, G.1    Perdisci, R.2    Zhang, J.3    Lee, W.4
  • 22
    • 70849095357 scopus 로고    scopus 로고
    • What's going on? Learning communication rules in edge networks
    • August
    • S. Kandula, R. Chandra, and D. Katabi. What's going on? Learning communication rules in edge networks. In Proceedings of ACM SIGCOMM, August 2008.
    • (2008) Proceedings of ACM SIGCOMM
    • Kandula, S.1    Chandra, R.2    Katabi, D.3
  • 27
    • 85076740771 scopus 로고    scopus 로고
    • WebProphet: Automating performance prediction for web services
    • Z. Li, M. Zhang, Z. Zhu, Y. Chen, A. G. Greenberg, and Y.-M. Wang. WebProphet: Automating performance prediction for web services. In NSDI, volume 10, 2010.
    • (2010) NSDI , vol.10
    • Li, Z.1    Zhang, M.2    Zhu, Z.3    Chen, Y.4    Greenberg, A.G.5    Wang, Y.-M.6
  • 31
    • 84861608741 scopus 로고    scopus 로고
    • NSDMiner: Automated discovery of network service dependencies
    • A. Natarajan, P. Ning, Y. Liu, S. Jajodia, and S. E. Hutchinson. NSDMiner: Automated discovery of network service dependencies. In INFOCOM, pages 2507-2515, 2012.
    • (2012) INFOCOM , pp. 2507-2515
    • Natarajan, A.1    Ning, P.2    Liu, Y.3    Jajodia, S.4    Hutchinson, S.E.5
  • 32
    • 62849120844 scopus 로고    scopus 로고
    • A survey of techniques for internet traffic classification using machine learning
    • T. T. T. Nguyen and G. J. Armitage. A survey of techniques for internet traffic classification using machine learning. IEEE Communications Surveys and Tutorials, 10(1-4):56-76, 2008.
    • (2008) IEEE Communications Surveys and Tutorials , vol.10 , Issue.1-4 , pp. 56-76
    • Nguyen, T.T.T.1    Armitage, G.J.2
  • 33
    • 84984866623 scopus 로고    scopus 로고
    • Panda Security Report. 2013. http://press.pandasecurity.com/press-room/reports/.
    • (2013) Panda Security Report
  • 34
    • 84984908980 scopus 로고    scopus 로고
    • Botnet Pony 1.9 Malware. http://laboratoriomalware. blogspot.com/2013/01/botnet-pony-19-malware.html.
    • Botnet Pony 1.9 Malware
  • 35
    • 70350394979 scopus 로고    scopus 로고
    • Database intrusion detection using weighted sequence mining
    • A. Srivastava, S. Sural, and A. Majumdar. Database intrusion detection using weighted sequence mining. Journal of Computers, 1(4):8-17, 2006.
    • (2006) Journal of Computers , vol.1 , Issue.4 , pp. 8-17
    • Srivastava, A.1    Sural, S.2    Majumdar, A.3
  • 38
    • 33750283653 scopus 로고    scopus 로고
    • A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification
    • 5-16, Oct
    • N. Williams, S. Zander, and G. Armitage. A preliminary performance comparison of five machine learning algorithms for practical IP traffic flow classification. SIGCOMM Comput. Commun. Rev., 36(5):5-16, Oct. 2006.
    • (2006) SIGCOMM Comput. Commun. Rev , vol.36 , pp. 5
    • Williams, N.1    Zander, S.2    Armitage, G.3
  • 42
    • 84863160317 scopus 로고    scopus 로고
    • Die free or live hard? Empirical evaluation and new design for fighting evolving twitter spammers
    • Springer
    • C. Yang, R. C. Harkreader, and G. Gu. Die free or live hard? Empirical evaluation and new design for fighting evolving twitter spammers. In Recent Advances in Intrusion Detection, pages 318-337. Springer, 2011.
    • (2011) Recent Advances in Intrusion Detection , pp. 318-337
    • Yang, C.1    Harkreader, R.C.2    Gu, G.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.