Multiclass ELM Based Smart Trustworthy IDS for MANETs

摘要

The dynamic nature ofMANET makes it susceptible to several security breaches. A system that observes these kinds of unwanted activities is known as intrusion detection system (IDS). An IDS is responsible to alert the network, in case of any threat observation. This paper broadens the scope of IDS by considering intrusion response also. The proposed work is organized into several phases such as feature selection, trust degree computation, classification and decision making. Intelligent agents are employed to handle all the aforementioned phases. Features of KDD Cup ’99 are reduced from 41 to 17 to minimize the training time and to improve the accuracy of the system. Feature selection is achieved by information gain ratio. The trust degree is computed by the combination of packet delivery ratio, behavior and available energy of a node. The trust degree parameters are vital elements in the classification and the decision-making phase. Extreme learning machine (ELM) is employed as the classifier to categorize nodes into trustworthy, partially trustworthy and malicious. The performance of the system is evaluated in different scenarios such as with/without feature selection and with/without trust degree computation, with respect to detection accuracy, misclassification rate and detection time. The classification accuracy of SVM, MLP, ELM and ELM with trust is also compared.