Survey on SDN based network intrusion detection system using machine learning approaches
Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems...
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| Published in | Peer-to-peer networking and applications Vol. 12; no. 2; pp. 493 - 501 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2019
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works. |
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| AbstractList | Software Defined Networking Technology (SDN) provides a prospect to effectively detect and monitor network security problems ascribing to the emergence of the programmable features. Recently, Machine Learning (ML) approaches have been implemented in the SDN-based Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. A stream of advanced machine learning approaches – the deep learning technology (DL) commences to emerge in the SDN context. In this survey, we reviewed various recent works on machine learning (ML) methods that leverage SDN to implement NIDS. More specifically, we evaluated the techniques of deep learning in developing SDN-based NIDS. In the meantime, in this survey, we covered tools that can be used to develop NIDS models in SDN environment. This survey is concluded with a discussion of ongoing challenges in implementing NIDS using ML/DL and future works. |
| Author | Sultana, Nasrin Chilamkurti, Naveen Alhadad, Rabei Peng, Wei |
| Author_xml | – sequence: 1 givenname: Nasrin surname: Sultana fullname: Sultana, Nasrin organization: Department of Computer Science and IT, La Trobe University – sequence: 2 givenname: Naveen surname: Chilamkurti fullname: Chilamkurti, Naveen email: n.chilamkurti@latrobe.edu.au organization: Department of Computer Science and IT, La Trobe University – sequence: 3 givenname: Wei surname: Peng fullname: Peng, Wei organization: Department of Accounting and Business Analytics, La Trobe University – sequence: 4 givenname: Rabei surname: Alhadad fullname: Alhadad, Rabei organization: Department of Computer Science and IT, La Trobe University |
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| References | Chen C, Gong Y, Tian Y (2008) Semi-supervised learning methods for network intrusion detection. Int Conf Sys, Man Cybern, IEEE. https://doi.org/10.1109/ICSMC.2008.4811688 Nguyen HT, Petrovic S, Franke K (2010) A comparison of feature-selection methods for intrusion detection. In: Kotenko I, Skormin V (eds) Computer Network Security. MMM-ACNS 2010. Lecture Notes in Computer Science, vol 6258. Springer, Berlin, Heidelberg, pp 242–255 Braga R, Mota E, Passito A (2010) Lightweight DDoS flooding attack detection using NOX/OpenFlow. 35th Annual IEEE conference on local computer networks, Denver, Colorado Deep learning stand to benefit to data analytics and HPC expertise http://www.cio.com/article/3180184/analytics/deep-learning- stands-to- benefit-from-data-analytics-and-high-performance-computing-hpc-expertise.html. Accessed 3 July 2017 Mehdi SA, Khalid J, Khaiyam SA (2011) Revisiting traffic anomaly detection using software defined networking. In: Sommer R, Balzarotti D, Maier G (eds) Recent Advances in Intrusion Detection. RAID 2011. Lecture Notes in Computer Science, vol 6961. Springer, Berlin, Heidelberg Haweliya J, Nigam B (2014) Network intrusion detection using semi supervised support vector machine. Int J Comput Appl 85, 9 Tuan TA, Mhamdi L, Mclernon D, Zaidi SAR, Ghogho M (2016) Deep learning approach for network intrusion detection in software defined networking. Int Conf Wirel Netw Mob Commun. https://doi.org/10.1109/WINCOM.2016.7777224 Syarif I, Prugel-Bennett A, Wills G (2012) Unsupervised clustering approach for network anomaly detection. In: Benlamri R (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. Springer, Berlin, Heidelberg Eid HFA, Darwish A, Hassanien AE, Abraham A (2010) Principal components analysis and support vector machine based intrusion detection system. International conference intelligent systems design and applications (ISDA) Yan Q, Yu FR, Gong Q and Li J (2016) Software-defined networking (SDN) and distributed denial of service (DDoS) attacks in cloud computing environments: A survey, some research issues, and challenges. IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp 602–622 Firstquarter 2016. https://doi.org/10.1109/COMST.2015.2487361 Open Flow [Online]. Available: http://www.openflow.org/. Accessed 12 July 2017 Wang L, Jones R (2017) Big data analytics for network intrusion detection: a survey. Int J Netw Commun. https://doi.org/10.5923/j.ijnc.20170701.03 Creech G, Hu J (2013) Generation of a new IDS test dataset: time to retire the KDD collection. Wirel Commun Netw Conf (WCNC). https://doi.org/10.1109/WCNC.2013.6555301 Prete LR, Shinoda AA, Schweitzer CM, De Oliveira RLS (2014) Simulation in an SDN network scenario using the POX controller. 2014 I.E. Colombian Conference on Communications and Computing (COLCOM), Bogota, pp 1–6. https://doi.org/10.1109/ColComCon.2014.6860403 Kaur S, Singh J, Ghumman NS (2014) Network programmability using POX controller. International conference on communication, computing & systems, at SBS Staten technical campus, Ferozepur, Punjab, India, volume: 1 Hughes T, Mierle K (2013) Recurrent neural networks for voice activity detection IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, pp 7378–7382. https://doi.org/10.1109/ICASSP.2013.6639096 Kreutz D, Ramos FMV, Verissimo PE, Rothenberg CE, Azodolmolky S (2015) Software-defines network- a comprehensive survey. Published in Proceedings of the IEEE, 103, 1 Zanero S, Savaresi SM (2004) Unsupervised learning techniques for an intrusion detection system. In: Proceedings of the ACM symposium on applied computing. Pages 412–419 Almomani I, Al-Kasasbeh B, Al-Akhras M (2016) WSN-DS: a dataset for intrusion detection systems in wireless sensor networks. J Sens 16p POX. [Online]. Available: http://www.noxrepo.org/pox/about-pox. Accessed 12 July 2017 Open Networking Foundation (2014) SDN architecture, Issue 1 June 2014 ONF TR-502 Alom MZ, Bontupalli VR, Taha TM (2015) Intrusion detection using deep belief networks. Aerospace and electronics conference, NAECON. IEEE Convolutional Neural Networks (2017) http://eric-yuan.me/cnn/. Accessed 10 July 2017 Hewlett Packard Enterprise (2015) 2015 cost of cyber crime study: global, independently conducted by Ponemon institute LLC publication, Ponemon Institute research report. Avaiable https://www.accenture.com/t20170926T072837Z__w__/us-en/_acnmedia/PDF-61/Accenture-2017-CostCyberCrimeStudy.pdf. Accessed 26 June 2017 Zamani M, Movahedi M (2015) Machine learning techniques for intrusion detection. CoRR, arXiv preprint arXiv:1312.2177. 2017 Jan 9 Vyas A (2017) Deep learning in natural language processing” in mphasis, deep learning- NL_whitepaper Bakshi T (2017) State of the art and recent research advances in software defined networking. In Wireless Communications and Mobile Computing, 2017, 1530-8669, Hindawi Publishing Corporation Supervised and unsupervised machine learning algorithms http://machinelearningmastery.com/supervised-and-unsupervised-machine learning-algorithms/. Accessed 20 June 2017 EidHFSalamaMAHassanienAEKimTHBi-layer behavioral based feature selection approach for network intrusion classificationCommun Comput Inf Sci Book Ser2011259195203 TsaiCHsuYLinCLinWIntrusion detection by machine learning: a reviewExpert Syst Appl200936119941200010.1016/j.eswa.2009.05.029 Hasan MAM, Nasser M, Ahmad S, Molla KH (2016) Feature selection for intrusion detection using random forest. 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CoRR, arXiv preprint arXiv:1701.02145. 2017 Jan 9 HuFHaoQBaoKA survey on software-defined network and openFlow: from concept to implementationIEEE communication surveys & tutorial201416410.1109/SURV.2013.111313.00244 Alom MZ, Bontupall VR, Taha TM (2015) Intrusion detection using deep belief networks. In: Aerospace and electronics conference, NAECON Garcı´a-TeodoroaPDı´az-VerdejoJMacia´-Ferna’ndezGVa´zquezEAnomaly-based network intrusion detection: Techniques, systems and challengesJ Comput Secur2009281-2182810.1016/j.cose.2008.08.003 LeCun Y, Bengio Y, Hinton G (2015) Deep learning review. Weekly journal of science in nature international. Nature 521, doi: https://doi.org/10.1038/nature14539 Thaseen S, Kumar Ch (2013) An analysis of supervised tree based classifiers for intrusion detection system. In: Proceedings of the international conference on pattern recognition, informatics and mobile engineering (P RIME). 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International conference wireless networks and mobile communications (WINCOM) ShiraviAShiraviHTavallaeeMGhorbaniAAToward developing a systematic approach to generate benchmark datasets for intrusion detectionComput Secur201231335737410.1016/j.cose.2011.12.012 Nour M, Slay J (2016) The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison 630_CR27 630_CR28 630_CR29 630_CR3 C Tsai (630_CR18) 2009; 36 630_CR4 630_CR6 630_CR7 630_CR8 630_CR9 630_CR30 630_CR32 630_CR33 630_CR34 630_CR35 630_CR1 630_CR36 630_CR2 630_CR37 Y Lu (630_CR50) 2014; 49 630_CR16 630_CR17 KP Bennett (630_CR19) 2017; 28 A Shiravi (630_CR54) 2012; 31 630_CR20 630_CR21 630_CR22 P Garcı´a-Teodoroa (630_CR5) 2009; 28 630_CR23 630_CR24 630_CR25 630_CR26 630_CR49 U Fiore (630_CR31) 2013; 122 630_CR52 630_CR53 630_CR10 630_CR11 630_CR55 630_CR12 630_CR56 630_CR13 630_CR57 630_CR14 HF Eid (630_CR51) 2011; 259 630_CR58 630_CR15 630_CR59 630_CR38 630_CR39 F Hu (630_CR47) 2014; 16 630_CR40 630_CR41 630_CR42 630_CR43 630_CR44 630_CR45 630_CR46 630_CR48 |
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IEEE – reference: FioreUPalmieriFCastiglioneASantisADNetwork anomaly detection with the restricted Boltzmann machineNeurocomputing201312225132310.1016/j.neucom.2012.11.050 – reference: Haweliya J, Nigam B (2014) Network intrusion detection using semi supervised support vector machine. Int J Comput Appl 85, 9 – reference: Nour M, Slay J (2016) The evaluation of network anomaly detection systems: statistical analysis of the UNSW-NB15 data set and the comparison with the KDD99 data set. Inf Secur J: A Glob Perspec, pp 1–14 – reference: Jankowski D, Amanowwicz M (2016) On efficiency of selected machine learning algorithms for intrusion detection in software defined networks. Int J Electron Telecommun, 62(3):247–252 – reference: Syarif I, Prugel-Bennett A, Wills G (2012) Unsupervised clustering approach for network anomaly detection. In: Benlamri R (eds) Networked Digital Technologies. NDT 2012. Communications in Computer and Information Science, vol 293. 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| SubjectTerms | Artificial intelligence Challenges and Prospective Smart Solutions Communications Engineering Computer Communication Networks Computer networks Cybersecurity Engineering Environment models Information Systems and Communication Service Intrusion detection systems Machine learning Networks Signal,Image and Speech Processing Software-defined networking Special Issue on Software Defined Networking: Trends |
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