메뉴 건너뛰기




Volumn , Issue , 2011, Pages 251-256

Collective classification for unknown malware detection

Author keywords

Computer viruses; Data mining; Machine learning; Malware detection; Security

Indexed keywords

ACCURACY RATE; ANTI VIRUS; CLASSIFICATION ALGORITHM; COLLECTIVE LEARNING; EMPIRICAL VALIDATION; GLOBAL SECURITY; MACHINE-LEARNING; MALICIOUS CODES; MALWARE DETECTION; MALWARES; SECURITY; SEMI-SUPERVISED LEARNING;

EID: 80052494221     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (33)

References (28)
  • 1
    • 33644641221 scopus 로고    scopus 로고
    • On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining
    • Cano, J., Herrera, F., and Lozano, M. (2006). On the combination of evolutionary algorithms and stratified strategies for training set selection in data mining. Applied Soft Computing Journal, 6(3):323-332.
    • (2006) Applied Soft Computing Journal , vol.6 , Issue.3 , pp. 323-332
    • Cano, J.1    Herrera, F.2    Lozano, M.3
  • 15
    • 79958242266 scopus 로고    scopus 로고
    • Collective classification for text classification
    • Namata, G., Sen, P., Bilgic,M., and Getoor, L. (2009). Collective classification for text classification. Text Mining, pages 51-69.
    • (2009) Text Mining , pp. 51-69
    • Namata, G.1    Sen, P.2    Bilgic, M.3    Getoor, L.4
  • 19
    • 80052977357 scopus 로고    scopus 로고
    • Semisupervised learning for unknown malware detection
    • Abraham, A., Corchado, J., Gonzlez, S., and De Paz Santana, J., editors, Advances in Intelligent and Soft Computing,Springer Berlin / Heidelberg
    • Santos, I., Nieves, J., and Bringas, P. (2011). Semisupervised learning for unknown malware detection. In Abraham, A., Corchado, J., Gonzlez, S., and De Paz Santana, J., editors, International Symposium on Distributed Computing and Artificial Intelligence, volume 91 of Advances in Intelligent and Soft Computing, pages 415-422. Springer Berlin / Heidelberg.
    • (2011) International Symposium on Distributed Computing and Artificial Intelligence , vol.91 , pp. 415-422
    • Santos, I.1    Nieves, J.2    Bringas, P.3
  • 21
    • 0037806811 scopus 로고    scopus 로고
    • The boosting approach to machine learning: An overview
    • Schapire, R. (2003). The boosting approach to machine learning: An overview. Lecture Notes in Statistics, pages 149-172.
    • (2003) Lecture Notes in Statistics , pp. 149-172
    • Schapire, R.1
  • 25
    • 67650330214 scopus 로고    scopus 로고
    • Comparative analysis of regression and machine learning methods for predicting fault proneness models
    • Singh, Y., Kaur, A., and Malhotra, R. (2009). Comparative analysis of regression and machine learning methods for predicting fault proneness models. International Journal of Computer Applications in Technology, 35(2):183-193.
    • (2009) International Journal of Computer Applications in Technology , vol.35 , Issue.2 , pp. 183-193
    • Singh, Y.1    Kaur, A.2    Malhotra, R.3
  • 26
    • 0037387684 scopus 로고    scopus 로고
    • OFFSS: Optimal fuzzy-valued feature subset selection
    • Tsang, E., Yeung, D., and Wang, X. (2003). OFFSS: optimal fuzzy-valued feature subset selection. IEEE transactions on fuzzy systems, 11(2):202-213.
    • (2003) IEEE Transactions on Fuzzy Systems , vol.11 , Issue.2 , pp. 202-213
    • Tsang, E.1    Yeung, D.2    Wang, X.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.