The best results were achieved with 10-Fold Cross Validation with one hidden layer. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. This paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. In this paper a specialized dataset for WSN is developed to help better detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. Such applications have created various security threats, especially in unattended environments. Wireless Sensor Networks (WSN) have become increasingly one of the hottest research areas in computer science due to their wide range of applications including critical military and civilian applications.
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