Malware Detection in Big Data Using Fast Pattern Matching: A Hadoop Based Comparison on GPU

详细信息    查看全文

• 作者：Chhabi Rani Panigrahi (21)
  Mayank Tiwari (21)
  Bibudhendu Pati (22)
  Rajendra Prasath (23)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8891
• 期：1
• 页码：407-416
• 全文大小：175 KB
• 参考文献：1. Aho, A.V., Corasick, M.J.: Efficient string matching: An aid to bibliographic search. Communications of the ACM聽18, 333鈥?40 (1975) CrossRef
  2. Boyer, R.S., Moore, J.S.: A fast string searching algorithm. Communications of the ACM聽20 (1977)
  3. Wu, S., Manber, U.: A fast algorithm for multi-pattern searching, Univ. Arizona, Tucson, Report TR 94鈥?7 (1994)
  4. Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM聽13, 422鈥?26 (1970) CrossRef
  5. ClamAV project: Clamav virus database, http://www.clamav.net/download.html (last accessed: August 15, 2014)
  6. Kojm, T.: Clam-av, http://www.clamav.net (last accessed: August 15, 2014)
  7. Christodorescu, M., Jha, S., Seshia, S., Song, D., Bryant, R.: Semantics-aware malware detection. In: 2005 IEEE Symposium Security and Privacy (2005)
  8. Dai, S.Y., Kuo, S.Y.: Mapmoon: A host-based malware detection tool. In: Proceedings of the 13th Pacific Rim International Symposium, pp. 349鈥?56. IEEE Computer Society Press (2007)
  9. Brumley, D., Hartwig, C., Kang, M.G., Liang, Z., Newsome, J., Poosankam, P., Song, D., Yin, H.: Automatically identifying trigger- based behavior in malware. Botnet Detection聽36, 65鈥?8 (2008) CrossRef
  10. Xu, B., Zhou, X., Li, J.: Recursive shift indexing: a fast multi-pattern string matching algorithm. In: Proc. of the 4th International Conference on Applied Cryptography and Network Security (ACNS), pp. 64鈥?3. IEEE Computer Society Press (2006)
  11. Fisk, M., Varghese, G.: An analysis of fast string matching applied to content-based forwarding and intrusion detection, Technical Report CS2001-0670, University of California San Diegoy (2002)
  12. Wikipedia: Map-reduce programming, wikispace, http://map-reduce.wikispaces.asu.edu (last Accessed: August 15, 2014)
  
• 作者单位：Chhabi Rani Panigrahi (21)
  Mayank Tiwari (21)
  Bibudhendu Pati (22)
  Rajendra Prasath (23)
  
  21. Dept. of Information Technology, C.V. Raman College of Engineering, Bhubaneswar, Odisha, 752 054, India
  22. Dept. of Computer Science and Engineering, C.V. Raman College of Engineering, Bhubaneswar, Odisha, 752 054, India
  23. Business Information Systems, University College Cork, Cork, Ireland
  
• ISSN：1611-3349

文摘

In big data environment, hadoop stores the data in distributed file systems called hadoop distributed file system and process the data using parallel approach. When the cloud users store unstructured data in cloud storage, it becomes very important for cloud providers to secure those data. To provide malware security, cloud service providers should scan the whole contents of the database, which is a very time intensive job. It may even take days to complete the tasks. The main aim of the proposed work is to reduce the processing time by introducing Graphics Processing Unit (GPU) in hadoop cluster. The proposed work integrates two text pattern matching algorithms with the map-reduce programming model for faster detection of malware in big data. The results of our study indicate that use of GPU decreases the processing time of text pattern matching algorithms in big data hadoop.