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Volumn 62, Issue , 2017, Pages 56-72

Modification of supervised OPF-based intrusion detection systems using unsupervised learning and social network concept

Author keywords

Centrality; Classification; Clustering; Optimum path forest; Prestige; Pruning; Social network analysis

Indexed keywords

ARTIFICIAL INTELLIGENCE; CLASSIFICATION (OF INFORMATION); COMPUTER CRIME; FORESTRY; GRAPHIC METHODS; INTRUSION DETECTION; LEARNING ALGORITHMS; LEARNING SYSTEMS; MERCURY (METAL); SOCIAL NETWORKING (ONLINE);

EID: 84994896280     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2016.08.027     Document Type: Article
Times cited : (58)

References (80)
  • 2
    • 58349096877 scopus 로고    scopus 로고
    • Data mining-based intrusion detectors
    • [2] Wu, S., Yen, E., Data mining-based intrusion detectors. Expert Syst. Appl. 36 (2009), 5605–5612, 10.1016/j.eswa.2008.06.138.
    • (2009) Expert Syst. Appl. , vol.36 , pp. 5605-5612
    • Wu, S.1    Yen, E.2
  • 3
    • 74149086366 scopus 로고    scopus 로고
    • On the symbiosis of specification-based and anomaly-based detection
    • [3] Stakhanova, N., Basu, S., Wong, J., On the symbiosis of specification-based and anomaly-based detection. Comput. Secur. 29 (2010), 253–268, 10.1016/j.cose.2009.08.007.
    • (2010) Comput. Secur. , vol.29 , pp. 253-268
    • Stakhanova, N.1    Basu, S.2    Wong, J.3
  • 4
    • 84970912506 scopus 로고    scopus 로고
    • An efficient hybrid intrusion detection system based on C5.0 and SVM
    • [4] Golmah, V., An efficient hybrid intrusion detection system based on C5.0 and SVM. Int. J. Database Theory Appl. 7 (2014), 59–70, 10.14257/ijdta.2014.7.2.06.
    • (2014) Int. J. Database Theory Appl. , vol.7 , pp. 59-70
    • Golmah, V.1
  • 5
    • 84888315965 scopus 로고    scopus 로고
    • A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
    • [5] Kim, G., Lee, S., Kim, S., A novel hybrid intrusion detection method integrating anomaly detection with misuse detection. Expert Syst. Appl. 41 (2014), 1690–1700, 10.1016/j.eswa.2013.08.066.
    • (2014) Expert Syst. Appl. , vol.41 , pp. 1690-1700
    • Kim, G.1    Lee, S.2    Kim, S.3
  • 7
    • 70350134739 scopus 로고    scopus 로고
    • The use of computational intelligence in intrusion detection systems: a review
    • [7] Wu, S.X., Banzhaf, W., The use of computational intelligence in intrusion detection systems: a review. Appl. Soft Comput. 10 (2010), 1–35, 10.1016/j.asoc.2009.06.019.
    • (2010) Appl. Soft Comput. , vol.10 , pp. 1-35
    • Wu, S.X.1    Banzhaf, W.2
  • 8
    • 36549085110 scopus 로고    scopus 로고
    • An active learning based TCM-KNN algorithm for supervised network intrusion detection
    • [8] Y., Li, L., Guo, An active learning based TCM-KNN algorithm for supervised network intrusion detection. Comput. Secur. 26 (2007), 459–467, 10.1016/j.cose.2007.10.002.
    • (2007) Comput. Secur. , vol.26 , pp. 459-467
    • Y, L.1    L, G.2
  • 9
    • 84888391834 scopus 로고    scopus 로고
    • Available on, (accessed 20.02.15)
    • [9] KDD Cup 99 Intrusion detection data set, Available on 〈http://kdd.ics.uci.edu/databases/kddcup99〉, (accessed 20.02.15).
    • KDD Cup 99 Intrusion detection data set
  • 10
    • 79951581599 scopus 로고    scopus 로고
    • Incremental SVM based on reserved set for network intrusion detection
    • [10] Yi, Y., Wu, J., Xu, W., Incremental SVM based on reserved set for network intrusion detection. Expert Syst. Appl. 38 (2011), 7698–7707, 10.1016/j.eswa.2010.12.141.
    • (2011) Expert Syst. Appl. , vol.38 , pp. 7698-7707
    • Yi, Y.1    Wu, J.2    Xu, W.3
  • 11
    • 84946616003 scopus 로고    scopus 로고
    • Normalized residual-based constant false-alarm rate outlier detection
    • [11] Ru, X., Liu, Z., Huang, Z., Jiang, W., Normalized residual-based constant false-alarm rate outlier detection. Pattern Recognit. Lett. 69 (2016), 1–7, 10.1016/j.patrec.2015.10.002.
    • (2016) Pattern Recognit. Lett. , vol.69 , pp. 1-7
    • Ru, X.1    Liu, Z.2    Huang, Z.3    Jiang, W.4
  • 12
    • 84900803761 scopus 로고    scopus 로고
    • Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks
    • [12] Rajasegarar, S., Gluhak, A., Imran, M.A., Nati, M., Moshtaghi, M., Leckie, C., Palaniswami, M., Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks. Pattern Recognit. 47 (2014), 2867–2879, 10.1016/j.patcog.2014.04.006.
    • (2014) Pattern Recognit. , vol.47 , pp. 2867-2879
    • Rajasegarar, S.1    Gluhak, A.2    Imran, M.A.3    Nati, M.4    Moshtaghi, M.5    Leckie, C.6    Palaniswami, M.7
  • 13
    • 77953128244 scopus 로고    scopus 로고
    • Semi-supervised outlier detection based on fuzzy rough C-means clustering
    • [13] Xue, Z., Shang, Y., Feng, A., Semi-supervised outlier detection based on fuzzy rough C-means clustering. Math. Comput. Simul. 80 (2010), 1911–1921, 10.1016/j.matcom.2010.02.007.
    • (2010) Math. Comput. Simul. , vol.80 , pp. 1911-1921
    • Xue, Z.1    Shang, Y.2    Feng, A.3
  • 14
    • 84905002847 scopus 로고    scopus 로고
    • Entropy-based outlier detection using semi-supervised approach with few positive examples
    • [14] Daneshpazhouh, A., Sami, A., Entropy-based outlier detection using semi-supervised approach with few positive examples. Pattern Recognit. Lett. 49 (2014), 77–84, 10.1016/j.patrec.2014.06.012.
    • (2014) Pattern Recognit. Lett. , vol.49 , pp. 77-84
    • Daneshpazhouh, A.1    Sami, A.2
  • 15
    • 0037209446 scopus 로고    scopus 로고
    • Host-based intrusion detection using dynamic and static behavioral models
    • [15] Yeung, D.Y., Ding, Y., Host-based intrusion detection using dynamic and static behavioral models. Pattern Recognit. 36 (2003), 229–243, 10.1016/S0031-3203(02)00026-2.
    • (2003) Pattern Recognit. , vol.36 , pp. 229-243
    • Yeung, D.Y.1    Ding, Y.2
  • 16
    • 40849099949 scopus 로고    scopus 로고
    • Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees
    • [16] Xiang, C., Yong, P.C., Meng, L.S., Design of multiple-level hybrid classifier for intrusion detection system using Bayesian clustering and decision trees. Pattern Recognit. Lett. 29 (2008), 918–924, 10.1016/j.patrec.2008.01.008.
    • (2008) Pattern Recognit. Lett. , vol.29 , pp. 918-924
    • Xiang, C.1    Yong, P.C.2    Meng, L.S.3
  • 17
    • 84941079784 scopus 로고    scopus 로고
    • A new approach to intrusion detection using artificial neural networks and fuzzy clustering
    • [17] Wang, G., Hao, J., Ma, J., Huang, L., A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Syst. Appl. 37 (2010), 6225–6232, 10.1016/j.eswa.2010.02.102.
    • (2010) Expert Syst. Appl. , vol.37 , pp. 6225-6232
    • Wang, G.1    Hao, J.2    Ma, J.3    Huang, L.4
  • 18
    • 84945936408 scopus 로고    scopus 로고
    • A novel SVM-kNN-PSO ensemble method for intrusion detection system
    • [18] Aburomman, A.A., Reaz, M.B.I., A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl. Soft Comput. 38 (2016), 360–372, 10.1016/j.asoc.2015.10.011.
    • (2016) Appl. Soft Comput. , vol.38 , pp. 360-372
    • Aburomman, A.A.1    Reaz, M.B.I.2
  • 19
    • 67049156806 scopus 로고    scopus 로고
    • Supervised pattern classification based on optimum-path forest
    • [19] Papa, J.P., Falcão, A.X., Suzuki, C.T.N., Supervised pattern classification based on optimum-path forest. Int. J. Imaging Syst. Technol. 19 (2009), 120–131.
    • (2009) Int. J. Imaging Syst. Technol. , vol.19 , pp. 120-131
    • Papa, J.P.1    Falcão, A.X.2    Suzuki, C.T.N.3
  • 22
    • 33644860127 scopus 로고    scopus 로고
    • A clustering-based method for unsupervised intrusion detections
    • [22] Jiang, S.Y., Song, X., Wang, H., Han, J.J., Li, Q.H., A clustering-based method for unsupervised intrusion detections. Pattern Recognit. Lett. 27 (2006), 802–810, 10.1016/j.patrec.2005.11.007.
    • (2006) Pattern Recognit. Lett. , vol.27 , pp. 802-810
    • Jiang, S.Y.1    Song, X.2    Wang, H.3    Han, J.J.4    Li, Q.H.5
  • 23
    • 68949161842 scopus 로고    scopus 로고
    • A triangle area based nearest neighbors approach to intrusion detection
    • [23] Tsai, C.F., Lin, C.Y., A triangle area based nearest neighbors approach to intrusion detection. Pattern Recognit. 43 (2010), 222–229, 10.1016/j.patcog.2009.05.017.
    • (2010) Pattern Recognit. , vol.43 , pp. 222-229
    • Tsai, C.F.1    Lin, C.Y.2
  • 24
    • 84933183260 scopus 로고    scopus 로고
    • CANN: An intrusion detection system based on combining cluster centers and nearest neighbors
    • [24] Lin, W.C., Ke, S.W., Tsai, C.F., CANN: An intrusion detection system based on combining cluster centers and nearest neighbors. Knowl. Based Syst. 78 (2015), 13–21, 10.1016/j.knosys.2015.01.009.
    • (2015) Knowl. Based Syst. , vol.78 , pp. 13-21
    • Lin, W.C.1    Ke, S.W.2    Tsai, C.F.3
  • 25
    • 84864563699 scopus 로고    scopus 로고
    • An optimum-path forest framework for intrusion detection in computer networks
    • [25] Pereira, C.R., Nakamura, R.Y.M., Costa, K.A.P., Papa, J.P., An optimum-path forest framework for intrusion detection in computer networks. Eng. Appl. Artif. Intell. 25 (2012), 1226–1234, 10.1016/j.engappai.2012.03.008.
    • (2012) Eng. Appl. Artif. Intell. , vol.25 , pp. 1226-1234
    • Pereira, C.R.1    Nakamura, R.Y.M.2    Costa, K.A.P.3    Papa, J.P.4
  • 26
    • 80052699760 scopus 로고    scopus 로고
    • Efficient supervised optimum-path forest classification for large datasets
    • [26] Papa, J.P., Falcão, A.X., Albuquerque, V.H.C., Tavares, J.M.R.S., Efficient supervised optimum-path forest classification for large datasets. Pattern Recognit. 45 (2012), 512–520, 10.1016/j.patcog.2011.07.013.
    • (2012) Pattern Recognit. , vol.45 , pp. 512-520
    • Papa, J.P.1    Falcão, A.X.2    Albuquerque, V.H.C.3    Tavares, J.M.R.S.4
  • 28
    • 84893770802 scopus 로고    scopus 로고
    • A comparison between k-optimum path forest and k-nearest neighbors supervised classifiers
    • [28] Souza, R., Rittner, L., Lotufo, R., A comparison between k-optimum path forest and k-nearest neighbors supervised classifiers. Pattern Recognit. Lett. 39 (2014), 2–10, 10.1016/j.patrec.2013.08.030.
    • (2014) Pattern Recognit. Lett. , vol.39 , pp. 2-10
    • Souza, R.1    Rittner, L.2    Lotufo, R.3
  • 30
    • 84961288567 scopus 로고    scopus 로고
    • A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks
    • [30] Costa, K.A.P., Pereira, L.A.M., Nakamura, R.Y.M., Pereira, C.R., Papa, J.P., Falcão, A.X., A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks. Inf. Sci. 294 (2015), 95–108, 10.1016/j.ins.2014.09.025.
    • (2015) Inf. Sci. , vol.294 , pp. 95-108
    • Costa, K.A.P.1    Pereira, L.A.M.2    Nakamura, R.Y.M.3    Pereira, C.R.4    Papa, J.P.5    Falcão, A.X.6
  • 32
    • 84912573275 scopus 로고    scopus 로고
    • Social activity and structural centrality in online social networks
    • [32] Klein, A., Ahlf, H., Sharma, V., Social activity and structural centrality in online social networks. Telemat. Inform. 32 (2015), 321–332, 10.1016/j.tele.2014.09.008.
    • (2015) Telemat. Inform. , vol.32 , pp. 321-332
    • Klein, A.1    Ahlf, H.2    Sharma, V.3
  • 33
    • 84871423186 scopus 로고    scopus 로고
    • C-index: a weighted network node centrality measure for collaboration competence
    • [33] Yan, X., Zhai, L., Fan, W., C-index: a weighted network node centrality measure for collaboration competence. J. Informetr. 7 (2013), 223–239, 10.1016/j.joi.2012.11.004.
    • (2013) J. Informetr. , vol.7 , pp. 223-239
    • Yan, X.1    Zhai, L.2    Fan, W.3
  • 34
    • 84925387554 scopus 로고    scopus 로고
    • A novel centrality method for weighted networks based on the Kirchhoff polynomial
    • [34] Qi, X., Fuller, E., Luo, R., Zhang, C., A novel centrality method for weighted networks based on the Kirchhoff polynomial. Pattern Recognit. Lett. 58 (2015), 51–60, 10.1016/j.patrec.2015.02.007.
    • (2015) Pattern Recognit. Lett. , vol.58 , pp. 51-60
    • Qi, X.1    Fuller, E.2    Luo, R.3    Zhang, C.4
  • 35
    • 84946495610 scopus 로고    scopus 로고
    • Fuzzy-rough community in social networks
    • [35] Kundu, S., Pal, S.K., Fuzzy-rough community in social networks. Pattern Recognit. Lett. 67 (2015), 145–152, 10.1016/j.patrec.2015.02.005.
    • (2015) Pattern Recognit. Lett. , vol.67 , pp. 145-152
    • Kundu, S.1    Pal, S.K.2
  • 36
    • 84885398842 scopus 로고    scopus 로고
    • Optimal local community detection in social networks based on density drop of subgraphs
    • [36] Qi, X., Tang, W., Wu, Y., Guo, G., Fuller, E., Zhang, C.Q., Optimal local community detection in social networks based on density drop of subgraphs. Pattern Recognit. Lett. 36 (2014), 46–53, 10.1016/j.patrec.2013.09.008.
    • (2014) Pattern Recognit. Lett. , vol.36 , pp. 46-53
    • Qi, X.1    Tang, W.2    Wu, Y.3    Guo, G.4    Fuller, E.5    Zhang, C.Q.6
  • 38
  • 39
    • 84888119718 scopus 로고    scopus 로고
    • Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization
    • [39] Bakshi, S., Jagadev, A.K., Dehuri, S., Wang, G., Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization. Appl. Soft Comput. 15 (2014), 21–29, 10.1016/j.asoc.2013.10.018.
    • (2014) Appl. Soft Comput. , vol.15 , pp. 21-29
    • Bakshi, S.1    Jagadev, A.K.2    Dehuri, S.3    Wang, G.4
  • 41
    • 84941155240 scopus 로고
    • Well separated clusters and optimal fuzzy partitions
    • [41] Dunn, J.C., Well separated clusters and optimal fuzzy partitions. J. Cybern. 4 (1974), 95–104, 10.1080/01969727408546059.
    • (1974) J. Cybern. , vol.4 , pp. 95-104
    • Dunn, J.C.1
  • 43
    • 0031166291 scopus 로고    scopus 로고
    • Cluster validation using graph theoretic concepts
    • [43] Pal, N.R., Biswas, J., Cluster validation using graph theoretic concepts. Pattern Recognit. 30 (1997), 847–857, 10.1016/S0031-3203(96)00127-6.
    • (1997) Pattern Recognit. , vol.30 , pp. 847-857
    • Pal, N.R.1    Biswas, J.2
  • 44
    • 0023453329 scopus 로고
    • Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
    • [44] Rousseeuw, P.J., Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20 (1987), 53–65, 10.1016/0377–0427(87)90125-7.
    • (1987) J. Comput. Appl. Math. , vol.20 , pp. 53-65
    • Rousseeuw, P.J.1
  • 46
    • 84973497973 scopus 로고    scopus 로고
    • CDV index: a validity index for better clustering quality measurement
    • [46] Yeh, J.H., Joung, F.J., Lin, J.C., CDV index: a validity index for better clustering quality measurement. J. Comput. Commun. 2 (2014), 163–171, 10.4236/jcc.2014.24022.
    • (2014) J. Comput. Commun. , vol.2 , pp. 163-171
    • Yeh, J.H.1    Joung, F.J.2    Lin, J.C.3
  • 49
    • 84994825680 scopus 로고
    • Social Network Analysis: Methods and Applications
    • Cambridge University Press New York
    • [49] Wasserman, S., Faust, K., Social Network Analysis: Methods and Applications. 1994, Cambridge University Press, New York.
    • (1994)
    • Wasserman, S.1    Faust, K.2
  • 53
    • 84896952109 scopus 로고    scopus 로고
    • Application of social network metrics to a trust-aware collaborative model for generating personalized user recommendations,
    • [53] I. Varlamis, M. Eirinaki, M. Louta, Application of social network metrics to a trust-aware collaborative model for generating personalized user recommendations, in: The Influence of Technology on Social Network Analysis and Mining, Springer, 2013, pp. 49–74, 10.1007/978-3-7091-1346-2_3.
    • (2013) The Influence of Technology on Social Network Analysis and Mining, Springer , pp. 49-74
    • Varlamis, I.1    Eirinaki, M.2    Louta, M.3
  • 61
    • 84865637442 scopus 로고    scopus 로고
    • Intrusion detection using reduced-size RNN based on feature grouping
    • [61] Sheikhan, M., Jadidi, Z., Farrokhi, A., Intrusion detection using reduced-size RNN based on feature grouping. Neural Comput. Appl. 21 (2012), 1185–1190, 10.1007/s00521-010-0487-0.
    • (2012) Neural Comput. Appl. , vol.21 , pp. 1185-1190
    • Sheikhan, M.1    Jadidi, Z.2    Farrokhi, A.3
  • 62
    • 79957736179 scopus 로고    scopus 로고
    • Distributed denial of service attack detection using an ensemble of neural classifier
    • [62] Raj Kumar, P.A., Selvakumar, S., Distributed denial of service attack detection using an ensemble of neural classifier. Comput. Commun. 34 (2011), 1328–1341, 10.1016/j.comcom.2011.01.012.
    • (2011) Comput. Commun. , vol.34 , pp. 1328-1341
    • Raj Kumar, P.A.1    Selvakumar, S.2
  • 63
    • 84872155077 scopus 로고    scopus 로고
    • Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems
    • [63] Raj Kumar, P.A., Selvakumar, S., Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems. Comput. Commun. 36 (2013), 303–319, 10.1016/j.comcom.2012.09.010.
    • (2013) Comput. Commun. , vol.36 , pp. 303-319
    • Raj Kumar, P.A.1    Selvakumar, S.2
  • 64
    • 84867826492 scopus 로고    scopus 로고
    • A-GHSOM: an adaptive growing hierarchical self organizing map for network anomaly detection
    • [64] Ippoliti, D., Zhou, X., A-GHSOM: an adaptive growing hierarchical self organizing map for network anomaly detection. J. Parallel Distrib. Comput. 72 (2012), 1576–1590, 10.1016/j.jpdc.2012.09.004.
    • (2012) J. Parallel Distrib. Comput. , vol.72 , pp. 1576-1590
    • Ippoliti, D.1    Zhou, X.2
  • 65
    • 80051793908 scopus 로고    scopus 로고
    • Data preprocessing for anomaly based network intrusion detection: a review
    • [65] Davis, J.J., Clark, A.J., Data preprocessing for anomaly based network intrusion detection: a review. Comput. Secur. 30 (2011), 353–375, 10.1016/j.cose.2011.05.008.
    • (2011) Comput. Secur. , vol.30 , pp. 353-375
    • Davis, J.J.1    Clark, A.J.2
  • 66
    • 85006208829 scopus 로고    scopus 로고
    • Intrusion detection model using fusion of chi-square feature selection and multi class SVM
    • (in press)
    • [66] S.T. Ikram, A.K. Cherukuri, Intrusion detection model using fusion of chi-square feature selection and multi class SVM, J. King Saud Univ. – Comput. Inf. Sci., 2016 (in press). 10.1016/j.jksuci.2015.12.004.
    • (2016) J. King Saud Univ. – Comput. Inf. Sci.
    • Ikram, S.T.1    Cherukuri, A.K.2
  • 67
  • 68
    • 84935592025 scopus 로고    scopus 로고
    • PCA filtering and probabilistic SOM for network intrusion detection
    • [68] De La Hoz, E., Hoz, E. De. La, Ortiz, A., Ortega, J., Prieto, B., PCA filtering and probabilistic SOM for network intrusion detection. Neurocomputing 164 (2015), 71–81, 10.1016/j.neucom.2014.09.083.
    • (2015) Neurocomputing , vol.164 , pp. 71-81
    • De La Hoz, E.1    Hoz, E.D.L.2    Ortiz, A.3    Ortega, J.4    Prieto, B.5
  • 70
    • 84959432825 scopus 로고    scopus 로고
    • A multi-step outlier-based anomaly detection approach to network-wide traffic
    • [70] Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K., A multi-step outlier-based anomaly detection approach to network-wide traffic. Inf. Sci. 348 (2016), 243–271, 10.1016/j.ins.2016.02.023.
    • (2016) Inf. Sci. , vol.348 , pp. 243-271
    • Bhuyan, M.H.1    Bhattacharyya, D.K.2    Kalita, J.K.3
  • 71
    • 79956097533 scopus 로고    scopus 로고
    • Mutual information-based feature selection for intrusion detection systems
    • [71] Amiri, F., Rezaei Yousefi, M.M., Lucas, C., Shakery, A., Yazdani, N., Mutual information-based feature selection for intrusion detection systems. J. Netw. Comput. Appl. 34 (2011), 1184–1199, 10.1016/j.jnca.2011.01.002.
    • (2011) J. Netw. Comput. Appl. , vol.34 , pp. 1184-1199
    • Amiri, F.1    Rezaei Yousefi, M.M.2    Lucas, C.3    Shakery, A.4    Yazdani, N.5
  • 72
    • 84929620760 scopus 로고    scopus 로고
    • Evolving statistical rulesets for network intrusion detection
    • [72] Rastegari, S., Hingston, P., Lam, C.-P., Evolving statistical rulesets for network intrusion detection. Appl. Soft Comput. 33 (2015), 348–359, 10.1016/j.asoc.2015.04.041.
    • (2015) Appl. Soft Comput. , vol.33 , pp. 348-359
    • Rastegari, S.1    Hingston, P.2    Lam, C.-P.3
  • 73
    • 3042829247 scopus 로고    scopus 로고
    • An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms
    • [73] Herlocker, J., Konstan, J.A., Riedl, J., An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf. Retr. 5 (2002), 287–310, 10.1023/A:1020443909834.
    • (2002) Inf. Retr. , vol.5 , pp. 287-310
    • Herlocker, J.1    Konstan, J.A.2    Riedl, J.3
  • 76
    • 84994874514 scopus 로고    scopus 로고
    • A review of DoS attacks in cloud computing
    • [76] Vidhya, V., A review of DoS attacks in cloud computing. IOSR J. Comput. Eng. 16 (2014), 32–35.
    • (2014) IOSR J. Comput. Eng. , vol.16 , pp. 32-35
    • Vidhya, V.1
  • 77
    • 84899669179 scopus 로고    scopus 로고
    • Improving domain action classification in goal-oriented dialogues using a mutual retraining method
    • [77] Seon, C.N., Lee, H., Kim, H., Seo, J., Improving domain action classification in goal-oriented dialogues using a mutual retraining method. Pattern Recognit. Lett. 45 (2014), 154–160, 10.1016/j.patrec.2014.03.021.
    • (2014) Pattern Recognit. Lett. , vol.45 , pp. 154-160
    • Seon, C.N.1    Lee, H.2    Kim, H.3    Seo, J.4
  • 78
    • 84958756389 scopus 로고    scopus 로고
    • Evaluating machine learning classification for financial trading: an empirical approach
    • [78] Gerlein, E.A., McGinnity, M., Belatreche, A., Coleman, S., Evaluating machine learning classification for financial trading: an empirical approach. Expert Syst. Appl. 54 (2016), 193–207, 10.1016/j.eswa.2016.01.018.
    • (2016) Expert Syst. Appl. , vol.54 , pp. 193-207
    • Gerlein, E.A.1    McGinnity, M.2    Belatreche, A.3    Coleman, S.4
  • 79
    • 84962205019 scopus 로고    scopus 로고
    • Ensemble based collaborative and distributed intrusion detection systems: a survey
    • [79] Folino, G., Sabatino, P., Ensemble based collaborative and distributed intrusion detection systems: a survey. J. Netw. Comput. Appl. 66 (2016), 1–16, 10.1016/j.jnca.2016.03.011.
    • (2016) J. Netw. Comput. Appl. , vol.66 , pp. 1-16
    • Folino, G.1    Sabatino, P.2
  • 80
    • 84954503459 scopus 로고    scopus 로고
    • Intrusion response systems: foundations, design, and challenges
    • [80] Inayat, Z., Gani, A., Anuar, N.B., Khurram Khan, M., Anwa, S., Intrusion response systems: foundations, design, and challenges. J. Netw. Comput. Appl. 62 (2016), 53–74, 10.1016/j.jnca.2015.12.006.
    • (2016) J. Netw. Comput. Appl. , vol.62 , pp. 53-74
    • Inayat, Z.1    Gani, A.2    Anuar, N.B.3    Khurram Khan, M.4    Anwa, S.5


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