Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 61-67.doi: 10.11896/j.issn.1002-137X.2017.6A.012
Previous Articles Next Articles
SUN Han-bo and FENG Guo-can
[1] WANG D,IRANI D,PU C.A study on evolution of email spam over fifteen years[C]∥2013 9th International Conference Conference on Collaborative Computing:Networking,Applications and Worksharing (Collaboratecom),2013.Austin,TX,USA:IEEE,2013:1-10. [2] 秦逸.基于行为的垃圾邮件检测技术[J].计算机科学,2012,39(11):86-89. [3] SAHAMI M,DUMAIS S,HECHERMAN D,et al.A Bayeslan approach to filtering junk E-Mail[C]∥Proceeding of Learning for Text Categorization Workshop-held in Conjunction with ICML/AAAI-98.Madison,WI,USA,1998:3256-3260. [4] 王青松,魏如玉.基于短语的贝叶斯中文垃圾邮件过滤方法[J].计算机科学,2016,43(4):256-259. [5] ALMEIDA T A,YAMAKAMI A.Advances in spam filtering techniques[J].Computational Intelligence for Privacy and Security,2012,394:199-214. [6] DRUCKER H,D W,VAPNIK V N.Support Vector Machines for Spam Categorization[J].IEEE Transactions on Neural Networks and Learning Systems,1999,20(5):1048-1054. [7] ANDROUTSOPOULOS I,PALIOURAS G, et al.Learning to Filter Unsolicited Commercial E-Mail[J].International Procee-dings of Computer Science & Information Tech,2004(2):1-52. [8] KOLCZ A,ALSPECTOR J.SVM-based Filtering of E-mailSpam with Content-specific Misclassification Costs[C]∥Proc of Textdm01 Workshop on Text Mining-held at the 2001 IEEE International Conference on Data Mining,2001.San Jose CA USA:IEEE,2001:1-14. [9] CARRERAS X,MARQUEZ L.Boosting Trees for Anti-SpamEmail Filtering[C]∥Proceedings of Euro Conference Recent Advances in NLP(RANLP-2001).TzigovChark,Bulgari:RANLP,2001:58-64. [10] NICHOLAS T.Using AdaBoost and Decision Stumps to Identify Spam E-mail[J].Natural Language Processing,2003:1-7. [11] 刘洋,杜孝平,周二胜,等.“垃圾邮件”的智能分析、过滤及Rough集讨论[C]∥中国计算机学会网络与数据通信学术会议,2002.武汉,2002:515-521. [12] 潘文锋.基于内容的垃圾邮件过滤研究[D].北京:中国科学院计算技术研究所,2004. [13] SOONTHORNPHISAJ N,CHAIKULSERIWAT K,TANG OP.Anti-Spam Filtering A Centroid-Based classification Approach[C]∥Proceedings of International Conference on Signal Processing (ICSP),2002.Pattaya Thailand:ICSP,2002:1096-1099. [14] ODA T,WHITE T.Increasing the accuracy of a spam-detecting artificial immune system[J].IEEE Transactions on Evolutionary Computation,2004,1:390-396. [15] 张泽明,罗文坚,王煦法.一种基于人工免疫的多层垃圾邮件过滤算法[J].电子学报,2006,34(9):1616-1620. [16] CHHABRA S,YERAZUNIS W,SIEFKES C.Spam filteringusing a Markov random field model with variable weighting schemas[C]∥Proceedings of 4th IEEE International Conference on Data Mining,2014.Hong Kong,China:IEEE,2014:347-350. [17] 李渊,廖闻剑,彭艳兵,等.复杂网络性质探讨及在垃圾邮件过滤中的运用[J].计算机科学,2013,40(S1):145-148. [18] ANDROUTSOPOULOS I,KOUTSIAS J,C HANDRINOS K,et al.An Experimental Comparison of Naive Bayesian and Keyword-Based Anti-Spam Filtering with Encrypted Personal E-mail Messages[C]∥Proceedings of the 23rd Annual International ACMSIGIR Conference on Research and Development in Information Retrieval(SIGIR),2000.Athens Greece:ACM,2000:160-167. [19] RENUKA D,HAMSAPRIYA T,CHAKKARAVARTHI M R,et al.Spam Classification Based on Supervised Learning Using Machine Learning Techniques[C]∥Proceedings of Process Automation,Control and Computing (PACC),2011.Coimbatore,India:PACC,2011:1-7. [20] RIJSBERGEN C J V,ROBERTSON S E,PORTER M F.New models in probabilistic information retrieval:British Library Research and Development Report,no.5587[R].Cambridge:Computer Laboratory University of Cambridge,1980. [21] SHEN H Y,LI Z.Leveraging Social Networks for EffectiveSpam Filtering[J].IEEE Transactions on Computers,2013,63(11):2743-2759. [22] DEBARR D,SUN H,WECHSLER H.Adversarial Spam Detection Using the Randomized Hough Transform Support Vector Machine[C]∥Proceedings of 2013 12th International Confe-rence on Machine Learning and Applications (ICMLA’12).Miami,FL,USA,2013:299-304. [23] SHARAM A,SAHNI S.A Comparative Study of Classification Algorithms for Spam Email Data Analysis[J].International Journal on Computer Science & Engineering,2011,3(5):111-117. [24] ZHOU B,YAO Y Y,LUO J G.A Three-Way Decision Ap-proach to Email Spam Filtering[C]∥Advances in Artificial Intelligence,Canadian Conference on Artificial Intelligence.Canadian,Ottawa,Canada,2010:28-39. [25] ZHANG Y D,WANG S G,PHILLIPS P,et al.Binary PSO with mutation operator for feature selection using decision tree applied to spam detection[J].Knowledge-Based Systems,2014,64:22-31. [26] KAYA Y,ERTUˇRUL F.A novel approach for spam email detection based on shifted binary patterns[J].Security & Communication Networks,2016,9(10):1216-1225. [27] ALQATAWNA J,FARIS H,JARADAT K,et al.ImprovingKnowledge Based Spam Detection Methods:The Effect of Malicious Related Features in Imbalance Data Distribution[J].International Journal of Communications,Network and System Sciences,2015,8(5):118-129. [28] NAKSOMBOON S,WATTANAPONGSAKORN N.Conside-ring behavior of sender in spam mail detection[C]∥Proceedings of International Conference on Networked Computing (INC).Gyeongju,Korea (South),2010:1-5. |
No related articles found! |
Viewed | ||||||||||||||||||||||||||||||||||||||||||||||
Full text 22
|
|
|||||||||||||||||||||||||||||||||||||||||||||
Abstract 149
|
|
|||||||||||||||||||||||||||||||||||||||||||||
Cited |
|
|||||||||||||||||||||||||||||||||||||||||||||
Shared | ||||||||||||||||||||||||||||||||||||||||||||||
Discussed |
|