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DOI: 10.14569/IJACSA.2022.0130497
PDF

CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks

Author 1: Salim Salmi
Author 2: Lahcen Oughdir

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 4, 2022.

  • Abstract and Keywords
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Abstract: A denial-of-service (DoS) attack is a coordinated attack by many endpoints, such as computers or networks. These attacks are often performed by a botnet, a network of malware-infected computers controlled by an attacker. The endpoints are instructed to send traffic to a particular target, overwhelming it and preventing legitimate users from accessing its services. In this project, we used a CNN-LSTM network to detect and classify DoS intrusion attacks. Attacks detection is considered a classification problem; the main aim is to clarify the attack as Flooding, Blackhole, Normal, TDMA, or Grayhole. This research study uses a computer- generated wireless sensor network-detection system dataset. The wireless sensor network environment was simulated using network simulator NS-2 based on the LEACH routing protocol to gather data from the network and preprocessed to produce 23 features classifying the state of the respective sensor and simulate five forms of Denial of Service (DoS) attacks. The developed CNN-LSTM model is further evaluated on 25 epochs with accuracy, Precision score, and Recall score of 0.944, 0.959, and 0.922, respectively, all on a scale of 0-1.

Keywords: Denial of Service (DoS); Wireless Sensor Networks (WSN); Convolutional Neural Network (CNN); Long Short-Term Memory (LSTM)

Salim Salmi and Lahcen Oughdir, “CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130497

@article{Salmi2022,
title = {CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130497},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130497},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Salim Salmi and Lahcen Oughdir}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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