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International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-5 Issue-47 October-2018
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DOI:10.19101/IJATEE.2018.546013
Paper Title : A survey on network intrusion detection system techniques
Author Name : K. NandhaKumar and S. Sukumaran
Abstract :

Security is the emerging trend in today’s modern world. Whole world is connected with some network capabilities and transmission of data becomes easier and faster. Nowadays, several places were implemented with network like schools, banks; offices etc. and many individuals are adopted with social network media. Several techniques were developed for improving the security aspects for network related issues. But still, vulnerable attacks are taken place and dominate the security aspects to pertain their strength towards various kinds of attack possibilities. For this reason, several network intrusion detection systems (NIDS) were proposed to protect computers as well as networks. It safeguards data integrity, system availability, and confidentiality from several kinds of attacks. In this paper, we study about the various types of network attacks and intrusion detection system to prevent from these attacks. Also, challenges that are faced by NIDS are discussed and comparison of different techniques and analysis are given in detail. The performance accuracy of each classifier that is previously proposed is comprised.
Keywords : Network security, Network intrusion detection system (NIDS), Network attacks, Deep learning.
Cite this article : K. NandhaKumar and S. Sukumaran, " A survey on network intrusion detection system techniques " , International Journal of Advanced Technology and Engineering Exploration (IJATEE), Volume-5, Issue-47, October-2018 ,pp.385-393.DOI:10.19101/IJATEE.2018.546013
References :
[1]Jyothi V, Addepalli SK, Karri R. DPFEE: A high performance scalable pre-processor for network security systems. IEEE Transactions on Multi-Scale Computing Systems. 2018; 4(1):55-68.
[Crossref] [Google Scholar]
[2]Zha Y, Li J. CMA: a reconfigurable complex matching accelerator for wire-speed network intrusion detection. IEEE Computer Architecture Letters. 2018; 17(1):33-6.
[Crossref] [Google Scholar]
[3]Tsikoudis N, Papadogiannakis A, Markatos EP. LEoNIDS: a low-latency and energy-efficient network-level intrusion detection system. IEEE Transactions on Emerging Topics in Computing. 2016; 4(1):142-55.
[Crossref] [Google Scholar]
[4]Liu J, Zhang S, Sun W, Shi Y. In-vehicle network attacks and countermeasures: challenges and future directions. IEEE Network. 2017; 31(5):50-8.
[Crossref] [Google Scholar]
[5]Zou CC, Duffield N, Towsley D, Gong W. Adaptive defense against various network attacks. IEEE Journal on Selected Areas in Communications. 2006; 24(10):1877-88.
[Crossref] [Google Scholar]
[6]Yang C, Feng L, Zhang H, He S, Shi Z. A novel data fusion algorithm to combat false data injection attacks in networked radar systems. IEEE Transactions on Signal and Information Processing over Networks. 2018; 4(1):125-36.
[Crossref] [Google Scholar]
[7]Yin D, Shen Y, Liu C. Attribute couplet attacks and privacy preservation in social networks. IEEE Access. 2017; 5:25295-305.
[Crossref] [Google Scholar]
[8]Deng S, Gao X, Lu Z, Gao X. Packet injection attack and its defense in software-defined networks. IEEE Transactions on Information Forensics and Security. 2018; 13(3):695-705.
[Crossref] [Google Scholar]
[9]Wu SX, Banzhaf W. The use of computational intelligence in intrusion detection systems: a review. Applied Soft Computing. 2010; 10(1):1-35.
[Crossref] [Google Scholar]
[10]Zhengbing H, Zhitang L, Junqi W. A novel network intrusion detection system (NIDS) based on signatures search of data mining. In proceedings of the 1st international conference on forensic applications and techniques in telecommunications, information, and multimedia and workshop 2008 . ICST.
[Google Scholar]
[11]Liu RT, Huang NF, Kao CN, Chen CH, Chou CC. A fast pattern-match engine for network processor-based network intrusion detection system. In international conference information technology: coding and computing, 2004 (pp. 97-101). IEEE.
[Crossref] [Google Scholar]
[12]Subaira AS, Anitha P. Efficient classification mechanism for network intrusion detection system based on data mining techniques: a survey. In international conference on intelligent systems and control 2014 (pp. 274-80). IEEE.
[Crossref] [Google Scholar]
[13]Bhuyan MH, Bhattacharyya DK, Kalita JK. Network anomaly detection: methods, systems and tools. Communications Surveys & Tutorials. 2014; 16(1):303-36.
[Crossref] [Google Scholar]
[14]Macia-Perez F, Mora-Gimeno FJ, Marcos-Jorquera D, Gil-Martinez-Abarca JA, Ramos-Morillo H, Lorenzo-Fonseca I. Network intrusion detection system embedded on a smart sensor. IEEE Transactions on Industrial Electronics. 2011; 58(3):722-32.
[Crossref] [Google Scholar]
[15]Kabir MF, Hartmann S. Cyber security challenges: an efficient intrusion detection system design. In international young engineers forum 2018 (pp. 19-24). IEEE.
[Crossref] [Google Scholar]
[16]Koo TM, Chang HC, Hsu YT, Lin HY. Malicious website detection based on honeypot systems. In international conference on advances in computer science and engineering 2013 (pp. 76-82). Atlantis Press.
[Google Scholar]
[17]Barghi MN, Hosseinkhani J, Keikhaee S. An effective web mining-based approach to improve the detection of alerts in intrusion detection systems. International Journal of Advanced Computer Science and Information. 2015; 4(1):38-45.
[Google Scholar]
[18]Kar D, Panigrahi S, Sundararajan S. SQLiDDS: SQL injection detection using query transformation and document similarity. In international conference on distributed computing and internet technology 2015 (pp. 377-90). Springer, Cham.
[Crossref] [Google Scholar]
[19]Friedberg I, Skopik F, Settanni G, Fiedler R. Combating advanced persistent threats: from network event correlation to incident detection. Computers & Security. 2015; 48:35-57.
[Crossref] [Google Scholar]
[20]Kour H, Sharma LS. Tracing out cross site scripting vulnerabilities in modern scripts. International Journal of Advanced Networking and Applications. 2016; 7(5):2862-7.
[Google Scholar]
[21]Dong B, Wang X. Comparison deep learning method to traditional methods using for network intrusion detection. In proceedings of ICCSN 2016 (pp. 581-5). IEEE.
[Crossref] [Google Scholar]
[22]Alrawashdeh K, Purdy C. Toward an online anomaly intrusion detection system based on deep learning. In international conference on machine learning and applications 2016 (pp. 195-200). IEEE.
[Crossref] [Google Scholar]
[23]Wang Y, Cai WD, Wei PC. A deep learning approach for detecting malicious JavaScript code. Security and Communication Networks. 2016; 9(11):1520-34.
[Crossref] [Google Scholar]
[24]Javaid A, Niyaz Q, Sun W, Alam M. A deep learning approach for network intrusion detection system. In proceedings of the EAI international conference on bio-inspired information and communications technologies 2016 (pp. 21-6). ICST.
[Crossref] [Google Scholar]
[25]Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX. Deep learning and its applications to machine health monitoring: a survey. IEEE Transactions on Neural Networks and Learning Systems. 2016.
[Google Scholar]
[26]Kim J, Shin N, Jo SY, Kim SH. Method of intrusion detection using deep neural network. In international conference on big data and smart computing 2017 (pp. 313-6). IEEE.
[Crossref] [Google Scholar]
[27]Gao N, Gao L, Gao Q, Wang H. An intrusion detection model based on deep belief networks. In international conference on advanced cloud and big data 2014 (pp. 247-52). IEEE.
[Crossref] [Google Scholar]
[28]Hore P, Hall LO, Goldgof DB. Single pass fuzzy c means. In international fuzzy systems conference 2007 (pp. 1-7). IEEE.
[Crossref] [Google Scholar]
[29]Li T, Li Q, Zhu S, Ogihara M. A survey on wavelet applications in data mining. ACM SIGKDD Explorations Newsletter. 2002; 4(2):49-68.
[Crossref] [Google Scholar]
[30]Guan H, Turk M. The hierarchical isometric self-organizing map for manifold representation. In conference on computer vision and pattern recognition 2007 (pp. 1-8). IEEE.
[Crossref] [Google Scholar]
[31]Tan PN, Steinbach M, Kumar V. Data mining cluster analysis: basic concepts and algorithms. Introduction to Data Mining. 2013.
[Google Scholar]
[32]Tsang IW, Kwok JT, Cheung PM. Core vector machines: fast SVM training on very large data sets. Journal of Machine Learning Research. 2005; 6:363-92.
[Google Scholar]
[33]Chauvin Y, Rumelhart DE. Backpropagation: theory, architectures, and applications. Psychology Press; 2013.
[Google Scholar]
[34]Fleizach C, Fukushima S. A naive Bayes classifier on 1998 KDD Cup.
[Google Scholar]
[35]Tian W, Liu J. Network intrusion detection analysis with neural network and particle swarm optimization algorithm. In Chinese control and decision conference 2010 (pp. 1749-52). IEEE.
[Crossref] [Google Scholar]
[36]Cleetus N, Dhanya KA. Multi-objective functions in particle swarm optimization for intrusion detection. In international conference on advances in computing, communications and informatics 2014 (pp. 387-92). IEEE.
[Crossref] [Google Scholar]
[37]Shin YB, Kita E. Solving two-dimensional packing problem using particle swarm optimization. Computer Assisted Methods in Engineering and Science. 2017; 19(3):241-55.
[Google Scholar]
[38]Aljarah I, Ludwig SA. Mapreduce intrusion detection system based on a particle swarm optimization clustering algorithm. In congress on evolutionary computation 2013 (pp. 955-62). IEEE.
[Crossref] [Google Scholar]
[39]Bratton D, Kennedy J. Defining a standard for particle swarm optimization. In swarm intelligence symposium 2007(pp. 120-7). IEEE.
[Crossref] [Google Scholar]
[40]Altwaijry H, Algarny S. Bayesian based intrusion detection system. Journal of King Saud University-Computer and Information Sciences. 2012; 24(1):1-6.
[Crossref] [Google Scholar]
[41]Panda M, Patra MR. Network intrusion detection using naive Bayes. International journal of computer science and network security. 2007; 7(12):258-63.
[Google Scholar]
[42]Peddabachigari S, Abraham A, Thomas J. Intrusion detection systems using decision trees and support vector machines. International Journal of Applied Science and Computations, USA. 2004; 11(3):118-34.
[Google Scholar]
[43]Villalba LJ, Castro JD, Orozco AL, Puentes JM. Malware detection system by payload analysis of network traffic. In international workshop on recent advances in intrusion detection 2012 (pp. 397-8). Springer, Berlin, Heidelberg.
[Crossref] [Google Scholar]