The attacker quickly affected the routing performance and drops the all packets that contain some data for receiver. This research proposed the Sybil Detection and Prevention (SDP) against Sybil attack. The property of this attack is to reply with every neighbors through multiple recognition (MR) value of itself i.e. fake identity, fake generated specification of itself in dynamic network. Routing protocols for MANET must handle obsolete routing information to hold the dynamically changeable topology. Incorrect routing information accomplished by malicious nodes to extent drop of packets, be considered malicious information. Whereas there are adequately many correct nodes, the SDP is able to find routes that deviates from these compromise nodes and provides secure path in between source to designation. The SDP has detected the malicious nodes and capture the malicious information of MR value generated in MANET. The SDP has immobilized the malicious functioning of Sybil attacker and enhance routing performance in presence of attacker. The better routing performance is devalued through performance parameters such as throughput and packets drop. The proposed scheme is improves throughput, minimizes data loss and provides secure routing.
Machine learning is a concerned with the design and development of algorithms. Machine learning is a programming approach to achieve optimization. Classification is the prediction approach in data mining techniques. Decision tree algorithm is the most common classifier to build tree because of it is easier to implement and understand. Attribute selection is a concept by which to select more significant attributes in the given datasets. This Paper proposed a novel hybrid approach with a combination of rough set and Random Forest algorithm called Rough Set based Random Forest Classifier (RSRF Classifier) which is used to deal with uncertainties, vagueness, and ambiguity associated with datasets. In this approach, the selection of significant attributes based on rough set theory as an input to Random Forest classifier for constructing the decision tree which is more efficient and scalable approach as compare to related work for lymph disease diagnosis studies.
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