Efficient Model for Intrusion Detection using Enhanced Classification Technique
Elsayed A.Elhafeez, Amr M. Gody, Tamer M. Barakat, Ayman. I. Madbouly "Efficient Model for Intrusion Detection using Enhanced Classification Technique", International Journal of Engineering Trends and Technology (IJETT), V51(2),70-77 September 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
False alerts are the most major problem that disturbs network administrator. In spite of the intelligent methods and strategy used by intrusion detection system, elimination of false alerts is still a big challenge. Due to the huge amount of information transmitted through the network, the traffic contains a big amount of redundant and duplicated information. That leads to bias the classifier and decrease classification accuracy and increase false alerts. So, we proposed an enhanced model to eliminate false alerts whether it was false positive or false negative alerts and increase the accuracy of intrusion detection system.
 R. Bace, Intrusion detection. Sams Publishing, 2000.
 R. Bace and P. Mell, ?NIST special publication on intrusion detection systems, DTIC Document, 2001
 Wagh, SharmilaKishor, Vinod K. Pachghare, and Satish R. Kolhe. "Survey on intrusion detection system using machine learning techniques." International Journal of Computer Applications 78.16 (2013).
 KDD Cup 99 Data set: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99
 Mei-Ling Shyu, K. Sarinnapakorn, I. Kuruppu-Appuhamilage, Shu-Ching Chen, LiWu Chang and T. Goldring, "Handling nominal features in anomaly intrusion detection problems," 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA`05), 2005, pp.55-62. doi: 10.1109/RIDE.2005.10
 RausheenBal, Sangeeta Sharma "Review on Meta Classification Algorithms using WEKA". International Journal of Computer Trends and Technology (IJCTT) V35(1):38-47, May 2016. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
 Fan, Rong-En, et al. "LIBLINEAR: A library for large linear classification." Journal of machine learning research 9.Aug (2008): 1871-1874.
 G. John and P. Langley, ?Estimating continuous distributions in Bayesian classifiers, Proc. Elev. Conf. …, pp. 338–345, 1995.
 Kshirsagar, Vivek, and Madhuri Joshi. "Enhancing Intrusion Detection System by Reducing the False Positives through Application of Various Data Mining Techniques." International Journal of Computer Science and Information Security 14.2 (2016): 76.
 Ali, Ghassan Ahmed. "Enhancing Intrusion Detection System (IDS) by Using Honeybee Concepts and Framework."
 Singhal, Pavan, and Gajendra Singh. "Enhanced Intrusion Detection System using Hybrid Machine Learning Approach." International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE) 3.7 (2014): pp-384.
 Abdullah, Azween Bin, and Long Zheng Cai. "Improving Intrusion Detection using Genetic Linear Discriminant Analysis." International Journal of Intelligent Systems and Applications in Engineering 3.1 (2015): 34-39.
 Dhakar, Mradul, and Akhilesh Tiwari. "A novel data mining based hybrid intrusion detection framework." Journal of Information and Computing Science9.1 (2014): 037-048.
 Golmah, Vahid. "An efficient hybrid intrusion detection system based on C5. 0 and SVM." International Journal of Database Theory and Application 7.2 (2014): 59-70.
 Chen, Shi, et al. "A graphical feature generation approach for intrusion detection." MATEC Web of Conferences. Vol. 44. EDP Sciences, 2016.
 Gholipour Goodarzi, Bahareh, Hamid Jazayeri, and Soheil Fateri. "Intrusion Detection System in Computer Network Using Hybrid Algorithms (SVM and ABC)." Journal of Advances in Computer Research 5.4 (2014): 43-52.
 Shawe, Rasha Thamer, and Safana H. Abbas. "Using An Improved Data Reduction Method in Intrusion detection system." Using An Improved Data Reduction Method in Intrusion detection system 3.1 (2017).
 Kim, Kyung-min, et al. "Evaluation of ACA-based Intrusion Detection Systems for Unknown-attacks." Probe 41 (2016): 0-84.
 J. R. Quinlan, C4.5: Programs for Machine Learning, vol. 1. 1993, p. 302.
 L. Breiman, ?Random forests, Mach. Learn., pp. 1–35, 2001.
IDS, false positive alerts, false negative alerts.