Intrusion Detection for MANET to Detect Unknown Attacks Using Genetic Algorithm

Traditional intrusion detection have a trouble dealing with lack of secure boundaries, threats from compromised nodes, lack of centralized management facility, restricted power supply and scalability. Due to these issues, we are motivated to propose efficient IDS, which involve a new technique to identify the anomalous activities in mobile ad-hoc networks. During this paper, we propose a Genetic based feature selection and rule evaluation process for anomaly detection. This process is effectively classified with new rules and also increases with high positive rate alarm. The new findings of our proposed work is effectively notice the anomalies with low false positive rate, high detection rate and attain the upper detection accuracy.