A Survey on Network Intrusion Detection

Authors(1) :-K. Veena

Network security is any activity designed to protect the usability and integrity of your network and data. It includes both hardware and software technologies. Effective network security manages access to the network. It targets a variety of threats and stops them from entering or spreading on your network. Network security combines multiple layers of defenses at the edge and in the network. Each network security layer implements policies and controls. Authorized users gain access to network resources, but malicious actors is blocked from carrying out exploits and threats. Two types of network includes wired and wireless network. The common vulnerability that exists in both wired and wireless networks is an “unauthorized access” to a network. An attacker can connect his device to a network though unsecure hub/switch port. In this regard, wireless network are considered less secure than wired network, because wireless network can be easily accessed without any physical connection. Network security is a big topic and is growing into a high profile Information Technology (IT) specialty area. Security-related websites are tremendously popular with savvy Internet users. The popularity of security-related certifications has expanded. Esoteric security measures like biometric identification and authentication have become commonplace in corporate America. Many organizations still implement security measures in an almost haphazard way, with no well-thought out plan for making all the parts fit together. Computer security involves many aspects, from protection of the physical equipment to protection of electronic bits and bytes that make up the information that resides on the network.

Authors and Affiliations

K. Veena
PG Scholar, Akshaya College of Engineering and Technology, Coimbatore, Tamil Nadu, India

Unauthorized Access, Savvy Internet Users, Intrusion Detection System, NIDS, HIDS

  1. B Dong and X. Wang, "Comparison deep learning method to traditional methods using for network intrusion detection," in Proc. 8th IEEE Int. Conf. Commun. Softw. Netw., Beijing, China, Jun. 2016, pp. 581-585.
  2. R Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. X. Gao, "Deep learning and its applications to machine health monitoring: A survey,"Submitted to IEEE Trans. Neural Netw. Learn. Syst., 2016. [Online].Available: http://arxiv.org/abs/1612.07640
  3. S Hou, A. Saas, L. Chen, and Y. Ye, "Deep4MalDroid: A Deep learningframework for android malware detection based on linux kernel systemcall graphs," in Proc. IEEE/WIC/ACM Int. Conf. Web Intell. Workshops,Omaha, NE, USA, Oct. 2016, pp. 104-111.
  4. IDC, "Executive summary: Data growth, business opportunities, and the IT imperatives. The digital universe of opportunities: Rich data and the increasing value of the internet of things," IDC, Framingham, MA, USA,Tech. Rep. IDC_1672, 2014. [Online]. Available: https://www.emc.com/ leadership/digital-universe/2014iview/executive-summary.htm
  5. Juniper Networks, "Juniper Networks, How many packets per secondper port are needed to achieve Wire-Speed?," 2015. [Online]. Available: https://kb.juniper.net/InfoCenter/index?page=content&id=KB14737
  6. I Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge,MA, USA: MIT Press, 2016. [Online]. Available: http://www.deeplearningbook.org
  7. L Deng, "Deep learning: Methods and applications," Found. Trends Signal Process., vol. 7, no. 3/4, pp. 197-387, Aug. 2014.
  8. P Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol,"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J. Mach. Learn. Res.,vol. 11, pp. 3371-3408, 2010.
  9. G E. Hinton and R. R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507,2006.
  10. Y. Wang, H. Yao, and S. Zhao, "Auto-encoder based dimensionality reduction," Neurocomputing, vol. 184, pp. 232-242, 2016.
  11. Z. Liang, G. Zhang, J. X. Huang, and Q. V. Hu, "Deep learning for healthcare decision making with EMRs," in Proc. IEEE Int. Conf. Bioinformat.Biomed., Nov. 2014, pp. 556-559.
  12. S. P. Shashikumar, A. J. Shah, Q. Li, G. D. Clifford, and S. Nemati,"A deep learning approach to monitoring and detecting atrial fibrillation using wearable technology," in Proc. IEEE EMBS Int. Conf. Biomed.Health Informat, FL, USA, 2017, pp. 141-144.
  13. F. Falcini, G. Lami, and A. M. Costanza, "Deep learning in automotive software," IEEE Softw., vol. 34, no. 3, pp. 56-63, May 2017. [Online]. Available: http://ieeexplore.ieee.org/document/7927925/
  14. A. Luckow, M. Cook, N. Ashcraft, E. Weill, E. Djerekarov, and B. Vorster,"Deep learning in the automotive industry: Applications and tools," in Proc. IEEE Int. Conf. Big Data, Dec. 2016, pp. 3759-3768. [Online].Available: http://ieeexplore.ieee.org/document/7841045/
  15. H. Lee, Y. Kim, and C. O. Kim, "A deep learning model for robust wafer fault monitoring with sensor measurement noise," IEEE Trans. Semicond.Manuf., vol. 30, no. 1, pp. 23-31, Feb. 2017.
  16. L. You, Y. Li, Y. Wang, J. Zhang, and Y. Yang, "A deep learning based RNNs model for automatic security audit of short messages," in Proc. 16th Int. Symp. Commun. Inf. Technol., Qingdao, China, Sep. 2016,pp. 225-229.
  17. R. Polishetty, M. Roopaei, and P. Rad, "A next-generation secure cloudbased deep learning license plate recognition for smart cities," in Proc.15th IEEE Int. Conf. Mach. Learn. Appl., Anaheim, CA, USA, Dec. 2016,pp. 286-293.
  18. K. Alrawashdeh and C. Purdy, "Toward an online anomaly intrusion detection system based on deep learning," in Proc. 15th IEEE Int. Conf. Mach. Learn. Appl., Anaheim, CA, USA, Dec. 2016,pp. 195-200.
  19. J. Kim, N. Shin, S. Y. Jo, and S. H. Kim, "Method of intrusion detection using deep neural network," in Proc. IEEE Int. Conf. Big Data Smart Comput., Hong Kong, China, Feb. 2017, pp. 313-316.
  20. A. Javaid, Q. Niyaz, W. Sun, and M. Alam, "A deep learning approach for network intrusion detection system," in Proc. 9th EAI Int.Conf. BioInspired Inf. Commun. Technol., 2016, pp. 21-26. [Online]. Available:http://dx.doi.org/10.4108/eai.3-12-2015.2262516
  21. S. Potluri and C. Diedrich, "Accelerated deep neural networks for enhanced intrusion detection system," in Proc. IEEE 21st Int. Conf. Emerg. Technol. Factory Autom., Berlin, Germany, Sep. 2016,pp. 1-8.
  22. C. Garcia Cordero, S. Hauke, M. Muhlhauser, and M. Fischer, "Analyzing flow-based anomaly intrusion detection using replicator neural networks," in Proc. 14th Annu. Conf. Privacy, Security. Trust, Auckland, New Zeland,Dec. 2016, pp. 317-324.
  23. T. A. Tang, L. Mhamdi, D. McLernon, S. A. R. Zaidi, and M. Ghogho, "Deep learning approach for network intrusion detection in software defined networking," in Proc. Int. Conf. Wireless Netw. Mobile Commun.,Oct. 2016, pp. 258-263.
  24. M.-J. Kang and J.-W. Kang, "Intrusion detection system using deep neural network for in-vehicle network security," PLoS One, vol. 11, no. 6,Jun. 2016, Art. no. e0155781.
  25. E. Hodo, X. J. A. Bellekens, A. Hamilton, C. Tachtatzis, and R. C. Atkinson, Shallow and deep networks intrusion detection system: A taxonomy and survey, Submitted to ACM Survey, 2017, [Online]. Available:http://arxiv.org/abs/1701.02145
  26. Q. Niyaz, W. Sun, and A. Y. Javaid, A deep learning based DDOS detection system in software-defined networking (SDN), Submitted to EAI Endorsed Transactions on Security and Safety, In Press, 2017, [Online].Available: http://arxiv.org/abs/1611.07400
  27. Y. Wang, W.-D. Cai, and P.-C. Wei, "A deep learning approach for detecting malicious JavaScript code," Security Commun. Netw., vol. 9, no. 11,pp. 1520-1534, Jul. 2016.
  28. H.-W. Lee, N.-R. Kim, and J.-H. Lee, "Deep neural network self-training based on unsupervised learning and dropout," Int. J. Fuzzy Logic Intell.Syst., vol. 17, no. 1, pp. 1-9, Mar. 2017. [Online]. Available:http://www.ijfis.org/journal/view.html?doi=10.5391/IJFIS.2017.17.1.1
  29. S. Choudhury and A. Bhowal, "Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection," in Proc. Int. Conf. Smart Technol. Manage. Comput., Commun., Controls, Energy Mater., May 2015, pp. 89-95.
  30. M. Anbar, R. Abdullah, I. H. Hasbullah, Y. W. Chong, and O. E. Elejla, "Comparative performance analysis of classification algorithms for intrusion detection system," in Proc. 14th Annu. Conf. Privacy, Security Trust,Dec. 2016, pp. 282-288.
  31. Y. Chang, W. Li, and Z. Yang, "Network intrusion detection based on random forest and support vector machine," in Proc. IEEE Int. Conf. Comput.Sci. Eng./IEEE Int. Conf. Embedded Ubiquitous Comput., Jul. 2017,pp. 635-638.
  32. Y. Y. Aung and M. M. Min, "An analysis of random forest algorithm based network intrusion detection system," in Proc. 18th IEEE/ACIS Int.Conf. Softw. Eng., Artif. Intell., Netw. Parallel/Distrib. Comput., Jun. 2017,pp. 127-132.
  33. L. Breiman, "Random forests," Mach. Learn., vol. 45, no. 1, pp. 5-32,2001.
  34. S. J. Stolfo, W. Fan, W. Lee, A. Prodromidis, and P. K. Chan, "Cost-based modeling for fraud and intrusion detection: Results from the JAM project," in Proc. DARPA Inf. Survivability Conf. Expo., 2000, pp. 130-144.
  35. M. Tavallaee, E. Bagheri, W. Lu, and A.-A. Ghorbani, "A detailed analysis of the KDD CUP 99 data set," in Proc. 2nd IEEE Symp. Comput. Intell. Security Defence Appl., 2009, pp. 53-58.
  36. J. McHugh, "Testing intrusion detection systems: A critique of the 1998 and 1999 DARPA intrusion detection system evaluations as performed by Lincoln Laboratory," ACM Trans. Inf. Syst. Security, vol. 3, no. 4,pp. 262-294, 2000.
  37. J. Kim, J. Kim, H. L. T. Thu, and H. Kim, "Long short term memory recurrent neural network classifier for intrusion detection," in Proc. Int.Conf. Platform Technol. Service, Feb. 2016, pp. 1-5.
  38. N. Gao, L. Gao, Q. Gao, and H. Wang, "An intrusion detection model based on deep belief networks," in Proc. 2nd Int. Conf. Adv. Cloud Big Data, Nov. 2014, pp. 247

Publication Details

Published in : Volume 4 | Issue 8 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 595-613
Manuscript Number : IJSRSET1848160
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

K. Veena, " A Survey on Network Intrusion Detection, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 4, Issue 8, pp.595-613, May-June-2018.
Journal URL : http://ijsrset.com/IJSRSET1848160

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