An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security


  • Hemanand D Professor, Department of Computer Science and Engineering, S.A. Engineering College (Autonomous), Thiruverkadu, Chennai-600077, Tamil Nadu, India
  • Mohankumar P Associate Professor School of Computer Science and Engineering, VIT University Vellore Tamil Nadu 632014,
  • Manoj Kumar N 3Associate Professor, Department of Electrical and Electronics Engineering, Panimalar Engineering College Chennai 600123, Tamil Nadu
  • Vaitheki S Assistant Professor, Department of Electronics and communication Engineering, P.S.R.R.College of Engineering, Sivakasi-626140, Taminadu, India
  • Saranya P Assistant Professor, Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu-600062, India.



Intrusion Detection System (IDS), Wireless Sensor Network (WSN), Min-Max Normalization, Data Preprocessing, Intelligent Prairie Dog Optimization (IPOD), and Deep auto Neural Network (DANN), and Attack Classification.


Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks less capable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, since it aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in the traditional works. However, its main drawbacks are computational burden, system complexity, poor network performance outcomes, and high false alarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly enhancing WSN security through the use of deep learning model. Here, the min-max normalization and data discretization operations are carried out to produce the preprocessed dataset. Then, an Intelligent Prairie Dog Optimization (IPDO) algorithm is used to reduce the dimensionality of features by identifying the best optimal solution with a higher convergence rate. Moreover, a Deep Auto-Neural Network (DANN) based classification method is used to properly forecast the class of data with less false alarms and higher detection rate. For evaluation, a thorough analysis is conducted to evaluate the performance and detection results of the proposed IPDO-DANN model.


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How to Cite

H. D, M. P, M. K. N, V. S, and S. P, “An Intelligent Prairie Dog Optimization (IPDO) and Deep Auto-Neural Network (DANN) based IDS for WSN Security”, Iraqi Journal For Computer Science and Mathematics, vol. 4, no. 4, pp. 30–42, Oct. 2023.
DOI: 10.52866/ijcsm.2023.04.04.004
Published: 2023-10-11