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基于域名信息的钓鱼URL探测 被引量:9

Phishing URL Detection Based on Domain Name Information
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摘要 提出一种基于域名信息的钓鱼URL探测方法。使用编辑距离寻找与已知正常域名相似的域名,根据域名信息提取域名单词最大匹配特征、域名分割特征和URL分割特征,利用上述特征训练分类器,由此判断其他URL是否为钓鱼URL。在真实数据集上的实验结果表明,该方法钓鱼URL正确检测率达94%。 This paper proposes a phishing URL detection method.It uses edit distance to find the domain names similar to normal domain names.According to domain name information,domain word maximum match features,domain name segmentation features and URL segmentation features are extracted to train a classifier,which can detect whether the URL is a phishing URL.Experiments on the real data set show that the method can achieve a precision of 94%.
出处 《计算机工程》 CAS CSCD 2012年第10期108-110,共3页 Computer Engineering
基金 国家242信息安全计划基金资助项目(242-2010A009)
关键词 钓鱼攻击 钓鱼URL探测 域名 支持向量机 编辑距离 phishing attack phishing URL detection domain name Support Vector Machine(SVM) edit distance
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参考文献11

  • 1Ma J,Saul L K,Savage S. Beyond Blacklists:Learning to Detect Malicious Web Sites from Suspicious URLs[A].France:Paris,2009.1245-1254.
  • 2Ma J,Saul L K,Savage S. Identifying Suspicious URLs:An Application of Large-scale Online Learning[A].Québec City,Canada,2009.681-688.
  • 3Blum A,Wardman B,Solorio T. Lexical Feature Based Phishing URL Detection Using Online Learning[A].Chicago,USA,2010.54-60.
  • 4Bilge L,Kirda E,Kruegel C. EXPOSURE:Finding Malicious Domains Using Passive DNS Analysis[A].Stanford,California:Stanford University,2011.1-17.
  • 5He Yuanchen,Zhong Zhenyu,Krasser S. Mining DNS for Malicious Domain Registrations[A].Chicago,USA,2010.1-6.
  • 6Cortes C,Vapnik V. Support Vector Networks[J].Machine Learning,1995,(03):273-297.
  • 7朱应武,杨家海,张金祥.基于流量信息结构的异常检测[J].软件学报,2010,21(10):2573-2583. 被引量:36
  • 8李昆仑,黄厚宽,田盛丰,刘振鹏,刘志强.模糊多类支持向量机及其在入侵检测中的应用[J].计算机学报,2005,28(2):274-280. 被引量:49
  • 9Liu Bing. Web Data Mining Exploring Hyperlinks,Contents and Usage Data[M].New York,USA:Springer-Verlag,2006.96-108.
  • 10Wong Pakkwong,Chan Chorkin. Chinese Word Segmentation Based on Maximum Matching and Word Binding Force[A].Copenhagen,Denmark,1996.200-203.

二级参考文献10

  • 1Hsu C.W., Lin C.J. A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 2002, 13(2): 415~425.
  • 2Weston J., Watkins C. Multi-class support vector machines. Department of Computer Science, Royal Holloway University of London Technical Report, SD-TR-98-04, 1998.
  • 3Kressel Ulrich. Pairwise classification and support vector machines. In: Schkopf B., Burges C.J.C., Smola A.J. eds. Advances in Kernel Methods--Support Vector Learning, Cambridge, MA: MIT Press, 1998, 255~268.
  • 4Platt J.C., Cristianini N., Shawe-Taylor J. Large margin DAG's for multiclass classification. Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2000, 12: 547~553.
  • 5Li Kun-Lun, Huang Hou-Kuan, Tian Sheng-Feng. A novel multi-class SVM classifier based on DDAG. In: Proceedings of IEEE ICMLC'02, Beijing, China, 2002, 3: 1203~1207.
  • 6Burges J.C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2): 121~167.
  • 7Vapnik V. Statistical Learning Theory. New York: Springer Verlag, 1998.
  • 8Corts C., Vapnik V. Support vector networks. Machine Learning, 1995, 20(3): 273~297.
  • 9程光,龚俭,丁伟.基于抽样测量的高速网络实时异常检测模型[J].软件学报,2003,14(3):594-599. 被引量:37
  • 10李昆仑,赵俊忠,黄厚宽,田盛丰.基于SVM技术的入侵检测[J].信息与控制,2003,32(6):495-499. 被引量:11

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