Detection of De-Authentication DoS Attacks in Wi-Fi Networks: A Machine Learning Approach

@article{Agarwal2015DetectionOD,
  title={Detection of De-Authentication DoS Attacks in Wi-Fi Networks: A Machine Learning Approach},
  author={Mayank Agarwal and Santosh Biswas and Sukumar Nandi},
  journal={2015 IEEE International Conference on Systems, Man, and Cybernetics},
  year={2015},
  pages={246-251}
}
Media Access Layer (MAC) vulnerabilities are the primary reason for the existence of the significant number of Denial of Service (DoS) attacks in 802.11 Wi-Fi networks. In this paper we focus on the de-authentication DoS (Deauth-DoS) attack in Wi-Fi networks. In Deauth-DoS attack an attacker sends a large number of spoofed de-authentication frames to the client (s) resulting in their disconnection. Existing solutions to mitigate Deauth-DoS attack rely on encryption, protocol modifications, 802… CONTINUE READING

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Key Quantitative Results

  • Experiments performed on in-house test bed shows that the proposed ML based IDS detects Deauth-DoS attack with precision (accuracy) and recall (detection rate) exceeding 96% mark.

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