Packet and flow based network intrusion dataset

Communications in Computer and Information Science

Volumn 306 CCIS, Issue , 2012, Pages 322-334

  Gogoi, Prasanta ^a   Bhuyan, Monowar H ^a   Bhattacharyya, D K ^a   Kalita, J K ^b  

^a TEZPUR UNIVERSITY   (India)

^b University of Colorado at Colorado Springs   (United States)

Author keywords

Anomaly; Dataset; Netflow; NIDS; Packet; Testbed

Indexed keywords

ANOMALY; DATA SETS; NETFLOWS; NIDS; PACKET;

COMPUTER APPLICATIONS; INTRUSION DETECTION; TESTBEDS;

DATA PROCESSING;

EID: 84868286263     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-32129-0_34     Document Type: Conference Paper

References (14)

1. 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)
2. 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)
3. 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)
4. 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)
5. 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)
6. 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)
7. 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)
8. 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)
9. 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)
10. 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)
11. Roesch, M.: Snort-lightweight intrusion detection for networks. In: Proceedings of the 13th USENIX Conference on System Administration, pp. 229-238. USENIX, Seattle (1999)
12. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley (2005)
13. 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/
14. 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)