Multi-Class Intrusion Detection using Two-Channel Color Mapping in IEEE 802.11 Wireless Network

Muhamad Erza Aminanto, R. Satrio Hariomurti Wicaksono, Achmad Eriza Aminanto, Harry Chandra Tanuwidjaja, Lin Yola, Kwangjo Kim

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusion Detection System (IDS) is one method to inspect the network traffic by leveraging state-of-the-art anomaly detection techniques. Deep learning models have been utilized to distinguish the benign and malicious traffic. However, projecting the tabular data into images before the image classification has been the main challenge of leveraging deep learning for IDS purposes.We propose the novel projection of tabular data into 2-coded color mapping for IDS purposes. The proposed method employs a feature selection method to ensure optimal dimensionality. We examined the different number of attribute subsets to obtain the relationship between the attributes. Furthermore, it takes advantage of the Convolutional Neural Network (CNN) model to classify the Wi-Fi attacks. We evaluate the proposed model using the most commonWi-Fi attacks dataset, AegeanWi-Fi Intrusion Dataset (AWID2). The proposed method achieved an F1 score of 99.73% and a false positive rate of 0.24%. This study highlights the importance of addressing the mapping procedures from tabular data into grid-based data before deep learning training and validates the effectiveness of CNN to detect multiple types of wireless network attacks.

Original languageEnglish
Pages (from-to)36791-36801
Number of pages11
JournalIEEE Access
Publication statusPublished - 1 Apr 2022


  • anomaly detection
  • convolutional neural network
  • Convolutional neural networks
  • Deep learning
  • Feature extraction
  • Image color analysis
  • intrusion detection system
  • Residual neural networks
  • wireless attacks
  • Wireless fidelity
  • Wireless networks


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