Real time intrusion detection system for ultra-high-speed big data environments

Journal of Supercomputing

Volumn 72, Issue 9, 2016, Pages 3489-3510

  Rathore, M Mazhar ^a   Ahmad, Awais ^a   Paul, Anand ^a  

^a Kyungpook National University   (South Korea)

Author keywords

Big data; Intrusion detection; Machine learning; Network; Threats

Indexed keywords

ARTIFICIAL INTELLIGENCE; BEHAVIORAL RESEARCH; CLASSIFICATION (OF INFORMATION); COMPUTER ARCHITECTURE; COMPUTER CRIME; DECISION MAKING; DECISION TREES; EFFICIENCY; INTRUSION DETECTION; LEARNING SYSTEMS; NETWORK ARCHITECTURE; NETWORK SECURITY; NETWORKS (CIRCUITS); SPEED;

ENTERPRISE NETWORKS; INTRUSION DETECTION SYSTEMS; MACHINE LEARNING APPROACHES; NUMBER OF PEOPLES; REAL TIME SOLUTION; SYSTEM ARCHITECTURES; THREATS; TRADITIONAL TECHNIQUES;

BIG DATA;

EID: 84959102038     PISSN: 09208542     EISSN: 15730484     Source Type: Journal    
DOI: 10.1007/s11227-015-1615-5     Document Type: Article

References (43)

1. Denning D (1986) An intrusion-detection model. In: IEEE computer society Symposium on research security and privacy, pp 118–131
2. Denning DE (1987) An intrusion-detection model. IEEE Trans Softw Eng 13(2):222–232. doi:10.1109/TSE.1987.232894
3. Butun I, Morgera SD, Sankar R (2014) A survey of intrusion detection systems in wireless sensor networks. IEEE Commun Surv Tutor 16(1):266–282
4. Ngadi M, Abdullah AH, Mandala S (2008) A survey on MANET intrusion detection. Int J Comput Sci Secur 2(1):1–11
5. Zhang Y, Lee W, Huang YA (2003) Intrusion detection techniques for mobile wireless networks. J Wirel Netw 9(5):545–556
6. Patcha A, Park JM (2007) An overview of anomaly detection techniques: existing solutions and latest technological trends. Elsevier J Comput Netw 51(12):3448–3470
7. Puttini R, Hanashiro M, Miziara F, de Sousa R, Garcia-Villalba L, Barenco C(2006) On the anomaly intrusion-detection in mobile ad hoc network environments. In: Proc. 11th IFIP TC6 international conference on personal wireless communications. Springer, pp 182–193
8. Engen, V.: Machine learning for network based intrusion. Ph.D. dissertation, Bournemouth Univ., Poole (2010)
9. ofcom (2013) Communications market report 2013 [Online]. http://www.ofcom.org.uk/cmruk/
10. Sagiroglu S, Sinanc D (2013) Big data: a review. In: Collaboration technologies and systems (CTS), 2013 International Conference on. IEEE, pp 42–47
11. Wu X, Zhu X, Wu G-Q, Ding W (2014) Data mining with big data. Knowl Data Eng IEEE Trans 26(1):97–107
12. Pires Jr. WR, de Paula Figueiredo TH, Wong HC, Loureiro AAF (2004) Malicious node detection in wireless sensor networks. In: Proc. 18th Int. Parallel Distrib. Process. Symp. (2004)
13. Rao R, Kesidis G (2003) Detecting malicious packet dropping using statistically regular traffic patterns in multihop wireless networks that are not bandwidth limited. In: Proc. IEEE GLOBECOM
14. Kayacik HG, Zincir-Heywood AN, Heywood MI (2005) Selecting features for intrusion detection: a feature relevance analysis on kdd99 intrusion detection datasets. In: Proceedings of the third annual conference on privacy, security and trust, Citeseer
15. Araujo N, de Oliveira R, Ferreira E-W, Shinoda A, Bhargava B (2010) Identifying important characteristics in the kdd99 intrusion detection dataset by feature selection using a hybrid approach. In: IEEE 17th international conference on telecommunications (ICT), pp 552–558. IEEE
16. Kantor P, Muresan G, Roberts F et al (2005) Analysis of three intrusion detection system benchmark datasets using machine learning algorithms. In: Intelligence and security informatics, sec. 3, p 363. Springer-Verlag, Berlin, Heidelberg
17. Abbes T, Bouhoula A, Rusinowitch M (2010) Efficient decision tree for protocol analysis in intrusion detection. Int J Secur Netw 5(4):220–235
18. Wagner C, François J, State R, Engel T (2011) Machine learning approach for IP-flow record anomaly detection. In: Proc. 10th International IFIP
19. Khan L, Awad M, Thuraisingham B (2007) A new intrusion detection system using support vector machines and hierarchical clustering. VLDB J 16(4):507–521
20. Schölkopf B, Platt JC, Shawe-Taylor JC, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
21. Muda Z, Yassin W, Sulaiman MN, Udzir NI (2011) A K-means and naive bayes learning approach for better intrusion detection. Inf Technol J 10(3):648–655
22. Gaddam SR, Phoha VV, Balagani KS (2007) K-Means+ID3: a novel method for supervised anomaly detection by cascading kmeans clustering and ID3 decision tree learning methods. IEEE Trans Knowl Data Eng 19(3):345–354
23. Cho SB (2002) Incorporating soft computing techniques into a probabilistic intrusion detection ystem. Syst Man Cybern Part C Appl Rev IEEE Trans 32(2):154–160
24. Yu Z, Tsai JJP, Weigert T (2007) An automatically tuning intrusion detection system. Syst Man Cybern Part B Cybern IEEE Trans 37(2):373–384
25. da Silva AP, Martins M, Rocha B, Loureiro A, Ruiz L, Wong HC (2005) Decentralized intrusion detection in wireless sensor networks. In: Proc. 1st ACM International workshop on quality of service and security in wireless and mobile networks (Q2SWinet ’05), pp 16–23. ACM Press
26. Wai FH, Aye YN, James NH (2005) Intrusion detection in wireless ad-hoc networks. CS4274, Introduction to Mobile Computing, term paper, School of Computing, National University of Singapore
27. Nadkarni K, Mishra A (2003) Intrusion detection in MANETs-the second wall of defense. In: Proc. 29th annual conference of the IEEE industrial electronics society
28. Francisco M-P et al (2011) Network intrusion detection system embedded on a smart sensor. Ind Electron IEEE Trans 58(3):722–732
29. Sequeira K, Zaki M (2002) ADMIT: anomaly-based data mining for intrusions. In: Proc. eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 386–395. ACM, New York
30. El-Khatib K (2010) Impact of feature reduction on the efficiency of wireless intrusion detection systems. Parallel Distrib Syst IEEE Trans 21(8):1143–1149
31. Tan Z, Nagar UT, Xiangjian He, Nanda P, Ren Ping Liu, Song Wang, Jiankun Hu (2014) Enhancing big data security with collaborative intrusion detection. Cloud Comput IEEE 1(3):27–33. doi:10.1109/MCC.2014.53
32. Huang J, Kalbarczyk Z, Nicol DM (2014) Knowledge discovery from big data for intrusion detection using LDA. In: Big data (BigData Congress), 2014 IEEE international congress on, June 27 2014-July 2 2014, pp 760–761. doi:10.1109/BigData.Congress.2014.111
33. Ahn S-H, Kim N-U, Chung T-M (2014) Big data analysis system concept for detecting unknown attacks. In: Advanced communication technology (ICACT), 2014 16th International Conference on, 16–19 Feb 2014, pp 269–272. doi:10.1109/ICACT.2014.6778962
34. Marchal S, Jiang X, State R, Engel T (2014) A Big data architecture for large scale security monitoring. In: Big data (BigData Congress), 2014 IEEE international congress on, June 27 2014–July 2 2014, pp 56–63. doi:10.1109/BigData.Congress.2014.18
35. I.S.T.G. MIT Lincoln Lab (2000) DARPA intrusion detection data sets. http://www.ll.mit.edu/mission/communications/ist/corpora/ideval/data/2000data.html
36. KDDcup99 (1999) Knowledge discovery in databases DARPA archive. http://www.kdd.ics.uci.edu/databases/kddcup99/task.html
37. NSL-KDD (2009) NSL-KDD data set for network-based intrusion detection systems. http://iscx.cs.unb.ca/NSL-KDD/
38. Al-Jarrah OY et al (2014) Machine-learning-based feature selection techniques for large-scale network intrusion detection. In: Distributed computing systems workshops (ICDCSW), 2014 IEEE 34th international conference on. IEEE
39. ENGEN (2010) Machine learning for network based intrusion detection. Doctoral dissertation, Bournemouth University
40. Zaman S, Karray F (2009) Features selection for intrusion detection systems based on support vector machines. In: Consumer communications and networking conference, 2009. CCNC 2009. 6th IEEE, pp 1–8
41. Fusco F, Deri L (2010) High speed network traffic analysis with commodity multi-core systems. ACM IMC 2010
42. Rathore MMU, Paul A, Ahmad A, Chen B, Huang B, Ji W (2015) Real-Time Big Data Analytical Architecture for Remote Sensing Application. Sel Top Appli Earth Observations Remote Sens, IEEE J 8(10):4610–4621. doi:10.1109/JSTARS.2015.2424683
43. Ahmad A, Paul A, Rathore MM (2016) An efficient divide-and-conquer approach for big data analytics in machine-to-machine communication. Neurocomputing 174:439–453