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Journal of Sensors
Volume 2016 (2016), Article ID 4731953, 16 pages
Research Article

WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks

1Computer Science Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
2Computer Science Department/Computer Information Systems Department, King Abdullah II School for Information Technology (KASIT), The University of Jordan, Amman, Jordan
3Computer Science Department, College of Computation and Informatics, Saudi Electronic University, Riyadh, Saudi Arabia

Received 25 March 2016; Accepted 28 August 2016

Academic Editor: Hana Vaisocherova

Copyright © 2016 Iman Almomani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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. Such applications have created various security threats, especially in unattended environments. To ensure the security and dependability of WSN services, an Intrusion Detection System (IDS) should be in place. This IDS has to be compatible with the characteristics of WSNs and capable of detecting the largest possible number of security threats. 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 paper considers the use of LEACH protocol which is one of the most popular hierarchical routing protocols in WSNs. A scheme has been defined to collect data from Network Simulator 2 (NS-2) and then processed to produce 23 features. The collected dataset is called WSN-DS. Artificial Neural Network (ANN) has been trained on the dataset to detect and classify different DoS attacks. The results show that WSN-DS improved the ability of IDS to achieve higher classification accuracy rate. WEKA toolbox was used with holdout and 10-Fold Cross Validation methods. The best results were achieved with 10-Fold Cross Validation with one hidden layer. The classification accuracies of attacks were 92.8%, 99.4%, 92.2%, 75.6%, and 99.8% for Blackhole, Flooding, Scheduling, and Grayhole attacks, in addition to the normal case (without attacks), respectively.