Cuckoo Search Algorithm and BF Tree Used for Anomaly Detection in Data Mining

  IJCOT-book-cover
 
International Journal of Computer & Organization Trends  (IJCOT)          
 
© 2019 by IJCOT Journal
Volume - 9 Issue - 4
Year of Publication : 2019
AuthorsShubhi Kulshrestha, Ankur Goyal
  10.14445/22492593/IJCOT-V9I4P303

MLA

MLA Style: Shubhi Kulshrestha, Ankur Goyal "Cuckoo Search Algorithm and BF Tree Used for Anomaly Detection in Data Mining" International Journal of Engineering Trends and Technology 9.4 (2019): 11-18.

APA Style: Shubhi Kulshrestha, Ankur Goyal (2019). Cuckoo Search Algorithm and BF Tree Used for Anomaly Detection in Data Mining International Journal of Engineering Trends and Technology, 9(4), 11-18.

Abstract

Anomaly detection is the new research topic of this new generation of researcher’s today. Defect detection is a domain, that is, the key towards impending data mining. The term mining model refers towards methods & algo’s that allow data towards be extracted & analyzed so that the rules & patterns that characterize the data can be found. Towards learn more about hidden structures & connections, the technology of data mining(DM)may be practical towards any type of data. In the current world, a lot of data is transported from one place towards another. Data that is transmitted or stored is subject towards attack. Although there are several techniques or applications available towards secure data, there are still ambiguities. It helps towards analyze resulting data & determine several types of attack data mining techniques that are open towards occurrence. Anomaly detection is technique of DMto detect astonishing or unanticipated behavior concealed in data that increases probabilities of penetrating or attacking. This learning proposes a model aimed at structure a NW penetration detection system by a machine learning (ML) algo & the system is primarily a fault-based penetration detection. In this paper, the feature selection Cuckoo search algo is performed, & the classification is mainly performed by the BF tree. It shows that the proposed work produces better results & the detection error attributes are more accurate.

References

[1] J. Huysmans, B. Baesens, D. Martens, K. Denys And J. Vanthienen, New Trends in Data Mining, TijdschriftvoorEconomieen Management, Vol. L, 4, 2005: 1-14.
[2] Lee, W., Stolfo, S. J., and Mok, K. W. 1999. A data mining framework for building intrusion detection models. In Proceedings of IEEE Symposium on Security and Privacy. 120–132.
[3] Patcha, A. and Park, J.2007. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Comput. Netw. 51, 12 (August 2007), 3448-3470. DOI=http://dx.doi.org/10.1016/j.comnet.2007.02.001
[4] Varun Chandola, Arindam Banerjee and Vipin Kumar, Anomaly Detection: A Survey, ACM Computing Surveys, Vol. 41, No. 3, Article 15, 2009: 1-58.
[5] Sahil Sanjay Tanpure, JayrajJagtap, Gunjan D. Patel, ApashabiPathan, Zishan Raja, Intrusion Detection System in Data Mining using Hybrid Approach, International Journal of Computer Applications (0975 – 8887) National Conference on Advances in Computing, Communication and Networking (ACCNet – 2016).
[6] Dedy Kurniadi, Sam FarisaChaerulHaviana, Data Mining Sales Optimizations Using Sequential Minimal Optimization Algorithm, Journal of Telematics and Informatics (JTI) Vol.4, No.2, September 2016, pp. 39~44 ISSN: 2303-3703.
[7] Singha, T., &Goswami, S. (2017). Classification in data mining using POEMs/GA algorithm. 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).doi:10.1109/icecds.2017.8390187.
[8] Saad Mohamed Ali Mohamed Gadal, Rania A. Mokhtar, Anomaly Detection Approach using Hybrid Algorithm of Data Mining Technique, 2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE), Khartoum, Sudan.
[9] Foroushani, Z. A., & Li, Y. (2018). Intrusion Detection System by Using Hybrid Algorithm of Data Mining Technique. Proceedings of the 2018 7th International Conference on Software and Computer Applications - ICSCA 2018.doi:10.1145/3185089.3185114
[10] Goeschel, K. (2016). Reducing false positives in intrusion detection systems using data-mining techniques utilizing support vector machines, decision trees, and naive Bayes for off-line analysis. SoutheastCon 2016. doi:10.1109/secon.2016.7506774.
[11] Sarah M. Shareef, Soukaena H. Hashim, Intrusion Detection System Based on Data Mining Techniques to Reduce False Alarm Rate, Engineering and Technology Journal Vol. 36, Part B, No. 2, 2018.
[12] Riyazahmed A. Jamadar, Network Intrusion Detection System Using Machine Learning, Indian Journal of Science and Technology, Vol 11(48), DOI: 10.17485/ijst/2018/v11i48/139802, December 2018.
[13] BlessyBoaz ,Kavitha.N, Anomaly Detection Based Intrusion Detection System Using Machine Learning Under Parallel Processing Framework, International Journal of Pure and Applied Mathematics Volume 118 No. 24 2018.

Keywords
Data mining, Intrusion Detection System, Anomaly IDS, K Means, SMO, Genetic Algorithm, Cuckoo Search Algorithm and BF tree.