Detection and Prevention of De-authentication Attack in Real-time Scenario

Author:

Abstract

Wireless Local Area Network (WLAN) is an infrastructure network in which nodes are connected to a centralized system to provide Internet access to mobile users by radio waves. But WLANs are vulnerable to Medium Access Control (MAC) layer Denial of Service (DoS) attacks due to the susceptibility of the management frames. An attacker can spoof the MAC address of the legitimate client and perform de-authentication attack to disconnect WLANs users from the access point. Many free tools are available in Kali Linux Operating System (OS) by which this attack can be performed and cause a security threat to WLAN users. The consequences of de-authentication DoS attack are frequent disconnection from Internet, traffic redirection, man-in-the-middle attack, and congestion. Despite enormous efforts in combating de-authentication DoS attack in the past decade, this attack is still a serious threat to the security of the cyber world. Medium Access Control Spoof Detection and Prevention (MAC SDP) DoS algorithm performs detection and prevention of de-authentication attack caused by spoofing MAC address. This algorithm is modified to make it more immune to the de-authentication attack and implemented in real-time scenario. The results show that the proposed technique increases the packet flow rate by 20.36%, reduces the packet loss by 95.71%, and reduces the down time and recovery time by 0.39 sec and 0.9 sec respectively as compared to MAC SDP DoS algorithm.

Publisher

Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP

Subject

Electrical and Electronic Engineering,Mechanics of Materials,Civil and Structural Engineering,General Computer Science

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine Learning Approaches for DDoS Attack Detection: Naive Bayes vs Logistic Regression;2023 Second International Conference On Smart Technologies For Smart Nation (SmartTechCon);2023-08-18

2. Network Intrusion Detection System for Feature Extraction Based on Machine Learning Techniques;2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA);2023-08-03

3. Deauthentication Attack Detection in the Wi-Fi network by Using ML Techniques;2022 Third International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE);2022-12-16

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