• M Lalli

Mobile ad-hoc network security problems are the subject of in depth analysis. A group of mobile nodes area unit connected to a set wired backbone. In MANET, the node themselves implement the network management in a very cooperative fashion. The entire nodes area unit accountable to create a constellation that is dynamically, modification it and conjointly the absence of any clear network boundaries. It tends to project a completely unique intrusion detection model for mobile ad-hoc network victimization. CP-KNN (Conformal Prediction K-Nearest Neighbor) algorithmic rule is to classify the audit knowledge for anomaly detection. The non-conformity score worth is employed to cut back the classification period of time for multi-level iteration. It is effectively notice anomalies with high true positive rate, low false positive rate and high confidence that the progressive of assorted anomaly detection ways. Additionally, it is interfered by “noisy” knowledge (unclean data), the projected technique is strong, effective and conjointly it retains its smart detection performance and to avoid the abnormal activity.

Mobile Ad Hoc Network, Genetic Algorithm, Conformal Prediction for K-Nearest Neighbor (CP-KNN), Relative Reduct.

"Intrusion Detection Rule Structure Generation Method for Mobile Ad Hoc Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.835-843, June 2019, Available :http://www.jetir.org/papers/JETIR1906U12.pdf

"Intrusion Detection Rule Structure Generation Method for Mobile Ad Hoc Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp835-843, June 2019, Available at : http://www.jetir.org/papers/JETIR1906U12.pdf