Deep Learning Perspectives to Detecting Intrusions in Wireless Sensor Networks


  • Ashwini B. Abhale Research Scholor, School of Information Technology & Engineering(SITE),Vellore Institute of Technology, Vellore, India
  • Jayaram Reddy A Assistant Professor, School of Information Technology & Engineering(SITE),Vellore Institute of Technology, Vellore, India


Deep learning, Anomaly detection, CNN, CNN-LSTM, GRU, RNN, LSTM


Due to the increasing number of communication protocols being used to send and receive data, security concerns have been raised about the unauthorized access of this data. To address these issues, the development of advanced IDS has been carried out. Deep learning is a type of machine learning that is composed of several neurons. Due to the increasing number of large-scale data sets and the success of deep learning in various fields, researchers have focused on detecting intrusions using deep learning. Due to the increasing number of data transmissions through various communication protocols, there has been a rise in security concerns about the security of these networks. This has prompted researchers to develop advanced IDSs that can detect unauthorized access. Besides having an effective network intrusion detection system(NIDS), continuous improvement is also required to ensure that the security of the network is maintained. Deep learning techniques are commonly used in the detection of network intrusions. It can also perform various tasks, such as analyzing and reporting on data. Due to its success in various fields, researchers have been focusing on developing deep learning techniques for detecting intrusions. This paper aims to review the current state of deep learning-based IDSs and compare with proposed modified algorithms with the previous ones.


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Wireless Sensor Network (img src-wikidata)




How to Cite

Abhale, A. B. ., & Reddy A, J. . (2023). Deep Learning Perspectives to Detecting Intrusions in Wireless Sensor Networks. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 18–26. Retrieved from



Research Article