The week's pick
Reseach Article
A Hybrid Data Mining Model for Intrusion Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 183 - Number 16 |
| Year of Publication: 2021 |
| Authors: Mahreen Nasir |
10.5120/ijca2021921489
|
Mahreen Nasir . A Hybrid Data Mining Model for Intrusion Detection. International Journal of Computer Applications. 183, 16 ( Jul 2021), 14-19. DOI=10.5120/ijca2021921489
Abstract
Network intrusion detection requires analysis of network data streams for identification of possible attacks. An Intrusion Detection System (IDS) is used to analyse such attacks and prevent future attacks. Main categories of IDS are anomaly detection and misuse detection. The limitation of anomaly based detection is high false positive rate whereas misuse detection based systems can only deal with known attack types. To address these, the main contribution of this paper is to propose a framework using hybrid approach based on clustering and classification methods for Intrusion Detection (CCID).
References
- Monowar H Bhuyan, Dhruba Kumar Bhattacharyya, and Jugal K Kalita. \Network anomaly detection: methods, systems and tools". In: Ieee communications surveys & tutorials 16.1 (2014), pp. 303-336.
- Misty Blowers and Jonathan Williams. \Machine learning applied to cyber operations". In: Network science and cybersecurity. Springer, 2014,pp. 155-175.
- Richard O Duda, Peter E Hart, and David G Stork. Pattern classication.John Wiley & Sons, 2012.
- Farah Jemili, Montaceur Zaghdoud, and Mohamed Ben Ahmed. “A Framework for an Adaptive Intrusion Detection System using Bayesian Network." In: ISI. 2007, pp. 66-70.
- Finn V. Jensen. Bayesian Networks and Decision Graphs. Berlin, Heidelberg: Springer-Verlag, 2001. isbn: 0387952594.
- Suleman Khan. Network forensics Review, taxonomy, and open challenges". en. In: Journal of Network and Computer Applications (2016),p. 22.
- Kingsly Leung and Christopher Leckie. “Unsupervised anomaly detection in network intrusion detection using clusters". In: Proceedings of the Twenty-eighth Australasian conference on Computer Science-Volume 38.Australian Computer Society, Inc. 2005, pp. 333-342.
- Yihua Liao and V Rao Vemuri. “Use of k-nearest neighbor classifier for intrusion detection". In: Computers & security 21.5 (2002), pp. 439-448.
- Safaa O Al-mamory and Firas S Jassim. “Evaluation of different data mining algorithms with KDD CUP 99 Data Set". In: Journal of University of Babylon 21.8 (2013), pp. 2663-2681.
- Karlton Sequeira and Mohammed Zaki. \ADMIT: anomaly-based data mining for intrusions". In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM. 2002, pp. 386-395.
- Richard Zuech, Taghi M Khoshgoftaar, and Randall Wald. “Intrusion detection and Big Heterogeneous Data: a Survey". en. In: (2015), p. 41.
- Salo, Fadi, et al. "Data mining techniques in intrusion detection systems: A systematic literature review." IEEE Access 6 (2018): 56046-56058.
- Agrawal, Diptee, and Chetan Agrawal. "A Review on Various Methods of Intrusion Detection System." Computer Engineering and Intelligent Systems 11.1 (2020).
- KDD Cup 1999, [online] Available: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (October, 2007)
- Wang, Wei, Xiaohong Guan, and Xiangliang Zhang. "Processing of massive audit data streams for real-time anomaly intrusion detection." Computer communications 31.1 (2008): 58-72.
Index Terms
Keywords