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.
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References
Gupta, S., Mamtora, R.: Intrusion detection system using wireshark. Int. J. Adv. Res. Comput. Sci. Soft. Eng 2, 34–36 (2011)
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)
Kaushik, S., Singhal, A.: Network security using cryptographic techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 2(12), 105–107 (2012)
Saliman, N., Farah, A., et al.: Software Implementation of Smith-Waterman Algorithm in FPGA
Aldwairi, M., Alsalman, R.: MALURLS: a lightweight malicious website classification based on url features. J. Emerg. Technol. Web Intell. 4(2), 128–133 (2012)
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)
Otgonbold, T.: ADAPT: an anonymous, distributed, and active probing-based technique for detecting malicious fast-flux domains (2014)
Sayamber, A.B., Dixit, A.M.: On URL classification. Int. J. Comput. Trends Technol. (IJCTT) 12
Zhou, Y., et al.: Hey, You, Get Off of My Market: Detecting Malicious Appsin Offcial and Alternative Android Markets (2012)
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)
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)
Liu, L., Han, Z.: Multi-block ADMM for Big Data Optimization in Modern Communication Networks. arXiv preprint arXiv:1504.01809 (2015)
Khonji, M., et al.: Phishing detection: a literature survey. IEEE Commun. Surv. Tutorials 15(4), 2091–2121 (2013)
Chu, W., et al.: Protect Sensitive Sites from Phishing Attacks Using Features Extractable from Inaccessible Phishing URLs. Microsoft Research Asia, Beijing (2011)
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)
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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
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DOI: https://doi.org/10.1007/978-981-10-2738-3_32
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