Abstract
With exponential growth in the number of computer applications and the size of networks, the potential damage that can be caused by attacks launched over the internet keeps increasing dramatically. A number of network intrusion detection methods have been developed with their respective strengths and weaknesses. The majority of research in the area of network intrusion detection is still based on the simulated datasets because of non-availability of real datasets. A simulated dataset cannot represent the real network intrusion scenario. It is important to generate real and timely datasets to ensure accurate and consistent evaluation of methods. We propose a new real dataset to ameliorate this crucial shortcoming. We have set up a testbed to launch network traffic of both attack as well as normal nature using attack tools. We capture the network traffic in packet and flow format. The captured traffic is filtered and preprocessed to generate a featured dataset. The dataset is made available for research purpose.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Adetunmbi, A.O., Falaki, S.O., Adewale, O.S., Alese, B.K.: Network intrusion detection based on rough set and k-nearest neighbour. International Journal of Computing and ICT Research 2, 60–66 (2008)
Amini, M., Jalili, R., Shahriari, H.R.: Rt-unnid: A practical solution to real- time network-based intrusion detection using unsupervised neural networks. Computers & Security 25(6), 459–468 (2006)
Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K.: NADO: network anomaly detection using outlier approach. In: Proceedings of the ACM International Conference on Communication, Computing & Security, New York, NY, USA, pp. 531–536 (2011)
Daniel, B., Julia, C., Sushil, J., Ningning, W.: Adam: a testbed for exploring the use of data mining in intrusion detection. SIGMOD Rec. 30(4), 15–24 (2001)
Gogoi, P., Borah, B., Bhattacharyya, D.K.: Anomaly detection analysis of intrusion data using supervised & unsupervised approach. Journal of Convergence Information Technology 5, 95–110 (2010)
Gogoi, P., Borah, B., Bhattacharyya, D.K.: Supervised anomaly detection using clustering based normal behaviour modeling. International Journal of Advances in Engineering Sciences 1, 12–17 (2011)
Gogoi, P., Borah, B., Bhattacharyya, D.K.: Network anomaly detection using unsupervised model. International Journal of Computer Applications (Special Issue on Network Security and Cryptography) NSC, 19–30 (2011)
Gogoi, P., Das, R., Borah, B., Bhattacharyya, D.K.: Efficient rule set generation using rough set theory for classification of high dimensional data. In: Proc. of Int’nl Conf. on Communication and Network Security (ICCNS 2011), Bhubaneswar, India, November 13-14, pp. 19–22 (2011)
Heady, R., Luger, G., Maccabe, A., Servilla, M.: The architecture of a network level intrusion detection system. Tech. rep., Computer Science Department, University of New Mexico, New Mexico (1990)
Paxson, V.: Bro: A system for detecting network intruders in real-time. In: Proceedings of the 7th USENIX Security Symposium, San Antonio,Texas (January 1998)
Roesch, M.: Snort-lightweight intrusion detection for networks. In: Proceedings of the 13th USENIX Conference on System Administration, pp. 229–238. USENIX, Seattle (1999)
Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the kdd cup 99 data set (2009), http://nsl.cs.unb.ca/NSL-KDD/
Tavallaee, M., Stakhanova, N., Ghorbani, A.A.: Toward credible evaluation of anomaly-based intrusion-detection methods. IEEE Transactions on Systems, Man, and Cybernetics, Part C 40, 516–524 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gogoi, P., Bhuyan, M.H., Bhattacharyya, D.K., Kalita, J.K. (2012). Packet and Flow Based Network Intrusion Dataset. In: Parashar, M., Kaushik, D., Rana, O.F., Samtaney, R., Yang, Y., Zomaya, A. (eds) Contemporary Computing. IC3 2012. Communications in Computer and Information Science, vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32129-0_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-32129-0_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32128-3
Online ISBN: 978-3-642-32129-0
eBook Packages: Computer ScienceComputer Science (R0)