Anomaly-Based Intrusion Detection System in WSN using DNN Algorithm

EasyChair Preprint 15974

Abstract

Intrusion detection systems (IDSs) are a necessary principle in WSN security, which can successfully prevent various hackers' and intruders' attempts to hack the network. In this research, we address the problem of intrusion detection in WSNs, including the difficulty in determining the appropriate dataset, the drawbacks of feature selection, the imbalanced dataset, and choosing the proper algorithms for the classification process. In this paper, we proposed the anomaly-based IDS model using the DNN algorithm and mutual information (MI) technology to select features. The proposed model has been implemented using the Python language used in the Anaconda platform and relying on the standard NSL-KDD dataset. The experimental results showed the capability of the proposed model to achieve high-performance accuracy in intrusion detection using the DNN algorithm compared to the state-of-the-art.

Keyphrases: DNN, IDS, WSN, deep learning, mutual information (MI) technology, security of WSNs

@booklet{EasyChair:15974,
  author    = {Belal Al-Fuhaidi and Zainab Farae and Wedad Al-Sorori and Naseebah Maqtary and Yahya Al-Ashmoery and Farouk Al-Fuhaidy and Sadiq Al-Taweel},
  title     = {Anomaly-Based Intrusion Detection System in WSN using DNN Algorithm},
  howpublished = {EasyChair Preprint 15974},
  year      = {EasyChair, 2025}}