Abstract
After digital revolution, large amount of data are produced from diverse networks from time to time. Hence security of this data is more important. So, there is a need to automate this security system. Intrusion detection systems are considered as the best solution to detect intrusions. Network intrusion detection systems (NIDS) are hired as a defense system to protect networks. Numerous techniques for the development of these defense systems are found in the literature. However, study on the enhancement of datasets used to train and test such security systems is also important. Improved datasets progress the detection capabilities for both offline and online intrusion detection models. Standard datasets like KDD 99, NSL-KDD cup 99 and DARPA 1999 are outdated and they don’t contain data of present attacks such as Denial of Service, therefore they are not suitable for evaluation. In this paper, in depth analysis of CIDDS-001 dataset is shown and the sightings are presented. In this paper, a gist of different papers available related to NIDS are given. This paper even compares all the research papers specifying their merits and demerits. This paper is concluded by providing a research method that can be applied to develop a better network intrusion detection system.
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References
Verma A, Ranga V Statistical analysis of CIDDS-001 dataset for network intrusion detection systems using distance-based machine learning, Department of Computer Engineering, NIT Kurukshetra, India
Medaglia CM, Serbanati A (2010) An overview of privacy and security issues in the Internet of Things. In: The Internet of Things, Springer, New York pp 389–395
Machine learning for network intrusion detection, Luke Hsiao, Stanford University. whsiao@stanford.edu, Stephen Ibanez, ibanez@stanford.edu
Shaya O (2008) Using machine learning in networks intrusion detection. https://doi.org/10.13140/rg.2.1.2586.0321
Liao H, Lin C, Lin Y, Tung K (2013) Intrusion detection system: a comprehensive review. J Netw Comput Appl 16–24. https://www.researchgate.net/publication/281451813_Using_Machine_Learning_in_Networks_Intrusion_Detection. Accessed 01 Jan 2019. 2013 (PDF) Using Machine Learning in Networks Intrusion Detection
Verma A, Ranga V (2018) On evaluation of network intrusion detection systems: statistical analysis of CIDDS-001 dataset using machine learning techniques, Department of Computer Engineering, National Institute of Technology, Kurukshetra, Haryana, India
Garcia-Teodoro P, Diaz-Verdejo J, Maciá-Fernández G, Vázquez E (2009) Anomaly-based network intrusion detection: techniques, systems and challenges. Comput Secur, 28(1):18–28
Sommer R, Paxson V (2010) Outside the closed world: on using machine learning for network intrusion detection. In: 2010 IEEE symposium on security and privacy (SP). IEEE, pp 305–316
Lippmann RP, Fried DJ, Graf I, Haines JW, Kendall KR, McClung D, Zissman MA (2000) Evaluating intrusion detection systems: the 1998 DARPA off-line intrusion detection evaluation. In: Proceedings of the DARPA information survivability conference and exposition, 2000, DISCEX 2000, vol 2. IEEE, pp 12–26
Flach P (2012) Machine learning: the art and science of algorithms that make sense of data. Cambridge University Press, Cambridge
Cover T, Hart P (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13(1):21–27
CIDDS-001 dataset (2017). https://www.hs-coburg.de/forschung-kooperation/forschungsprojekte-oeffentlich/ingenieurwissenschaften/cidds-coburg-intrusion-detection-data-sets.html
Hsiao L, Ibanez S. Machine Learning for Network Intrusion Detection, Stanford University
Alsallal M (2017) Applying machine learning to improve your intrusion detection system, 17 January
KarsligЕl ME, Yavuz AG, Güvensan MA, Hanifi K, Bank H (2017) Network intrusion detection using machine learning anomaly detection algorithms. In: 2017 25th signal processing and communications applications conference (SIU), Antalya. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7960616&isnumber=7960135
Pasocal C, Oliveira M, Valdas R, Filzmoser P, Salvador P, Pacheco A (2012) Robust feature selection and robust PCA for Internet traffic anomaly detection. In Proceedings IEEE INFOCOM, pp 1755–1763
Sommer R, Paxson V (2010) Outside the closed world: on using machine learning for network intrusion detection. In: IEEE symposium on security and privacy, pp 305–316
Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. In: IEEE communications surveys & tutorials, vol 18, no. 2, pp 1153–1176. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7307098&isnumber=7475979
Biswas S (2018) Intrusion detection using machine learning: a comparison study. Int J Pure Appl Math 118:101–114
Aggarwal P, Sharma SK (2015) Analysis of KDD dataset attributes-class wise for intrusion detection. Proc Comput Sci 57:842–851
Siddiqui MK, Naahid S (2013) Analysis of KDD CUP 99 dataset using clustering based data mining. Int J Database Theor Appl 6(5):23–34
Ingre B, Yadav A (2015) Performance analysis of NSL-KDD dataset using ANN. In: 2015 International conference on signal processing and communication engineering systems (SPACES). IEEE, pp 92–96
Moustafa N, Slay J (2015) The significant features of the UNSW-NB15 and the KDD99 data sets for network intrusion detection systems. In: 2015 4th International workshop on building analysis datasets and gathering experience returns for security (BADGERS). IEEE, pp. 25–31
KDD Cup 1999 (2014). http://kdd.ics.uci.edu/databases/kddcup99/
UNSW-NB15 dataset (2017). https://www.unsw.adfa.edu.au/australian-centre-for-cybersecurity/cybersecurity/ADFA-NB15-Datasets/
Parsazad S, Saboori E, Allahyar A (2012) Fast feature reduction in intrusion detection datasets. In: Proceedings of the 35th international convention MIPRO Opatija, Croatia. IEEE, pp 1023–1029
Rampure V, Tiwari A (2015) A rough set based feature selection on KDD CUP 99 data set. Int J Database Theor Appl 8(1):149–156
Hasan MAM, Nasser M, Ahmad S, Molla KI (2016) Feature selection for intrusion detection using random forest. J Inf Secur 7(03):129–140
Janarthanan T, Zargari S (2017) Feature selection in UNSW-NB15 and KDDCUP’99 datasets. In: Proceedings of 26th international symposium on industrial electronics (ISIE), Edinburgh, UK. IEEE, pp 1881–1886
Aminanto ME, Choi R, Tanuwidjaja HC, Yoo PD, Kim K (2018) Deep abstraction and weighted feature selection for Wi-Fi impersonation detection. IEEE Trans Inf Forensics Secur 13(3):621–636
AWID (2018) AWID dataset. Accessed 2 Jan 2018. http://icsdweb.aegean.gr/awid/download.html
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Rupa Devi, T., Badugu, S. (2020). A Review on Network Intrusion Detection System Using Machine Learning. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 4. Springer, Cham. https://doi.org/10.1007/978-3-030-24318-0_69
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