Skip to main content

Prediction of Malicious Domains Using Smith Waterman Algorithm

  • Conference paper
  • First Online:
Security in Computing and Communications (SSCC 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 625))

Included in the following conference series:

  • 866 Accesses

Abstract

IT security is an issue in today world. This is due to many reasons such as, malicious domains. Predicting the malicious domain in a set of domains is important. Here we have proposed a method for analysing such domains. In this method Wireshark is used for capturing the network packets. These packets are further given to client server machine and store in server database which makes an interface between the wireshark and machine. The data from the server database are then compared with the dictionary to predict the malicious websites. It is identified in such a way that if a word in a domain matches with any one of the dictionary word then it is considered as non-malicious websites others are malicious websites.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gupta, S., Mamtora, R.: Intrusion detection system using wireshark. Int. J. Adv. Res. Comput. Sci. Soft. Eng 2, 34–36 (2011)

    Google Scholar 

  2. Pottner, W.-B., Wolf, L.: IEEE 802.15. 4 packet analysis with wireshark and off-the-shelf hardware. In: Proceedings of the Seventh International Conference on Networked Sensing Systems (INSS2010), Kassel, Germany (2010)

    Google Scholar 

  3. Kaushik, S., Singhal, A.: Network security using cryptographic techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(12), 105–107 (2012)

    Google Scholar 

  4. Saliman, N., Farah, A., et al.: Software Implementation of Smith-Waterman Algorithm in FPGA

    Google Scholar 

  5. Aldwairi, M., Alsalman, R.: MALURLS: a lightweight malicious website classification based on url features. J. Emerg. Technol. Web Intell. 4(2), 128–133 (2012)

    Google Scholar 

  6. Park, D.: A study of packet analysis regarding a DoS attack in WiBro environments. IJCSNS Int. J. Comput. Sci. Netw. Secur. 8(12), 398–402 (2008)

    Google Scholar 

  7. Otgonbold, T.: ADAPT: an anonymous, distributed, and active probing-based technique for detecting malicious fast-flux domains (2014)

    Google Scholar 

  8. Sayamber, A.B., Dixit, A.M.: On URL classification. Int. J. Comput. Trends Technol. (IJCTT) 12

    Google Scholar 

  9. Zhou, Y., et al.: Hey, You, Get Off of My Market: Detecting Malicious Appsin Offcial and Alternative Android Markets (2012)

    Google Scholar 

  10. Dabir, A., Matrawy, A.: Bottleneck analysis of traffic monitoring using wireshark. In: 4th International Conference on Innovations in Information Technology, 2007. IIT 2007. IEEE (2007)

    Google Scholar 

  11. Hongke, H., Linhai, Q.: Application and research of multidimensional dataanalysis in power quality. In: 2010 International Conference on Computer Design and Applications (ICCDA), vol. 1. IEEE (2010)

    Google Scholar 

  12. Liu, L., Han, Z.: Multi-block ADMM for Big Data Optimization in Modern Communication Networks. arXiv preprint arXiv:1504.01809 (2015)

  13. Khonji, M., et al.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutorials 15(4), 2091–2121 (2013)

    Article  Google Scholar 

  14. Chu, W., et al.: Protect Sensitive Sites from Phishing Attacks Using Features Extractable from Inaccessible Phishing URLs. Microsoft Research Asia, Beijing (2011)

    Google Scholar 

  15. Jin, X., et al.: Social spam guard: a data mining based spam detection system for social media networks. In: 37th International Conference on Very Large Data Bases, Washington, 29 August 2011 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to B. Ashwini .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Nature Singapore Pte Ltd.

About this paper

Cite this paper

Ashwini, B., Menon, V.K., Soman, K.P. (2016). Prediction of Malicious Domains Using Smith Waterman Algorithm. In: Mueller, P., Thampi, S., Alam Bhuiyan, M., Ko, R., Doss, R., Alcaraz Calero, J. (eds) Security in Computing and Communications. SSCC 2016. Communications in Computer and Information Science, vol 625. Springer, Singapore. https://doi.org/10.1007/978-981-10-2738-3_32

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2738-3_32

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2737-6

  • Online ISBN: 978-981-10-2738-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics