Prediction of Malicious Domains Using Smith Waterman Algorithm

  • B. AshwiniEmail author
  • Vijay Krishna Menon
  • K. P. Soman
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 625)


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.


Domain Name System (DNS) Data acquisition Wireshark Malicious Smith-Waterman 


  1. 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. 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. 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. 4.
    Saliman, N., Farah, A., et al.: Software Implementation of Smith-Waterman Algorithm in FPGAGoogle Scholar
  5. 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. 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. 7.
    Otgonbold, T.: ADAPT: an anonymous, distributed, and active probing-based technique for detecting malicious fast-flux domains (2014)Google Scholar
  8. 8.
    Sayamber, A.B., Dixit, A.M.: On URL classification. Int. J. Comput. Trends Technol. (IJCTT) 12 Google Scholar
  9. 9.
    Zhou, Y., et al.: Hey, You, Get Off of My Market: Detecting Malicious Appsin Offcial and Alternative Android Markets (2012)Google Scholar
  10. 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. 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. 12.
    Liu, L., Han, Z.: Multi-block ADMM for Big Data Optimization in Modern Communication Networks. arXiv preprint arXiv:1504.01809 (2015)
  13. 13.
    Khonji, M., et al.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutorials 15(4), 2091–2121 (2013)CrossRefGoogle Scholar
  14. 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. 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

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • B. Ashwini
    • 1
    Email author
  • Vijay Krishna Menon
    • 1
  • K. P. Soman
    • 1
  1. 1.Centre for Computational Engineering and Networking (CEN)Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapetham, Amrita UniversityCoimbatoreIndia

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