K. Madhuri, Dr. A. Mummoorthi


A selection of candidate-based functions under supervision is still being proposed, namely the specification of flexible common information characteristics. FMIFS is definitely an improvement on MIFS and MMIFS. FMIFS proposes an amendment to the Battiti formula to reduce redundancy between features. FMIFS removes the required redundancy parameter in MIFS and MMIFS.FMIFS is definitely an improvement of MIFS and MMIFS. FMIFS proposes an amendment to the Battiti formula to reduce redundancy between features. FMIFS eliminates the necessary redundancy parameter in MIFS and MMIFS. Existing solutions are still unable to fully protect Internet applications and computer systems from the threats of online attack techniques constantly evolving, such as denial of service attacks, computer adware and spyware. Current network traffic data, often large, is a major challenge for IDS. The evaluation results showed that our formula selection jobs provide the most important functions for LSSVM-IDS to achieve greater accuracy, minimizing the computational cost in contrast to previous technical methods. This job selection formula is designed for information exchanged for linear and non-linear data functions. In this document, we recommend the format of information exchanged that analyzes the optimal characteristics of the classification. Its usefulness is assessed within the delivery network invasion. Repeated and irrelevant data properties caused a long-term condition in the network traffic classification. These features not only slow down the entire process of classification, but also prevent the workbook from making accurate decisions, especially when it comes to large data.


Linear Correlation Coefficient; Mutual Information; Intrusion Detection;


S.-W. Lin, Z.-J. Lee, S.-C. Chen, T.-Y. Tseng, Parameter determination of support vector machine and feature selection using simulated annealing approach, Applied soft computing 8 (4) (2008) 1505–1512.

Y.-I. Moon, B. Rajagopalan, U. Lall, Estimation of mutual information using kernel density estimators, Physical Review E 52 (3) (1995) 2318–2321.

A. M. Ambusaidi, X. He, P. Nanda, Unsupervised feature selection method for intrusion detection system, in: International Conference on Trust, Security and Privacy in Computing and Communications, IEEE, 2015.

R. Agarwal, M. V. Joshiy, Pnrule: A new framework for learning classier models in data mining (a case-study in network intrusion detection), Citeseer2000.

D. S. Kim, J. S. Park, Network-based intrusion detection with support vector machines, in: Information Networking, Vol. 2662, Springer, 2003, pp. 747–756.

Mohammed A. Ambusaidi, Member, IEEE, Xiangjian He*, Senior Member, IEEE,Priyadarsi Nanda, Senior Member, IEEE, and Zhiyuan Tan, Member, IEEE, “Building an intrusion detection system using afilter-based feature selection algorithm”, ieee transactions on computers,2016.

G. Kim, S. Lee, S. Kim, A novel hybrid intrusion detection method integrating anomaly detection with misuse detection, Expert Systems with Applications 41 (4) (2014) 1690–1700.

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