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Detection of Management-Frames-Based Denial-of-Service Attack in Wireless LAN Network Using Artificial Neural

Abdallah Elhigazi Abdallah1, Mosab Hamdan2,3, Mohammed S M Gismalla4

  • 1Faculty of Computer Science, Future University, Khartoum 10553, Sudan.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an artificial neural network (NN) to detect management-frames-based Denial of Service (DoS) attacks in Wireless Local Area Networks (WLANs). The NN scheme effectively identifies malicious frames, enhancing network security and reliability.

Keywords:
artificial neural networkdenial of service (DoS)media access control (MAC)wireless local area network (WLAN)

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Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Wireless Local Area Networks (WLANs) are widely adopted but vulnerable to security threats.
  • Denial of Service (DoS) attacks, particularly those exploiting management frames at the MAC layer, pose significant risks to WLANs.
  • Current security mechanisms lack robust defenses against these specific DoS attack vectors.

Purpose of the Study:

  • To design and develop an artificial neural network (NN) scheme for detecting management-frames-based DoS attacks in WLANs.
  • To enhance network performance by mitigating communication interruptions caused by fake de-authentication/disassociation frames.
  • To provide a more sophisticated and effective solution for securing wireless networks against DoS threats.

Main Methods:

  • Development of a novel artificial neural network (NN) scheme.
  • Utilizing machine learning techniques to analyze patterns and features in WLAN management frames.
  • Training the NN model to accurately identify malicious management frames indicative of DoS attacks.

Main Results:

  • The proposed NN scheme demonstrates superior effectiveness in detecting management-frames-based DoS attacks compared to existing methods.
  • Experimental results show a significantly increased true positive rate for attack detection.
  • A notable decrease in the false positive rate was observed, indicating higher accuracy.

Conclusions:

  • The developed NN scheme offers a promising approach to bolstering WLAN security against management-frames-based DoS attacks.
  • The technique effectively distinguishes legitimate from malicious frames, thereby improving network reliability.
  • This research contributes to enhancing the overall security and stability of wireless communication networks.