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An advanced actor critic deep reinforcement learning technique for gamification of WiFi environment

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Abstract

The Open System Interconnection Model’s physical layer implementation contains several networks including the wireless networks working system over radio waves. Since any device with wireless capabilities enabled can access the wireless network, hence most of them are protected by password and giving rise to the development of several security protocols over the time by the Wireless Fidelity (WiFi) Alliance. Currently, the most prevalent Wireless Protected Access (WPA/WPA2) systems are widely used across the globe despite the WPA3 model. The WPA/WPA2 uses a 256-bit pre-shared key also called as passphrase for authenticating users, hence like any other passphrase the shorter ones are vulnerable to attacks that can decrypt the hash formed out of handshake information. Thus, the security analyst should monitor the network from sniffing attacks during a handshake process and if the attack detects the user data then it tends to create several vulnerabilities. For this reason, in this paper, an Artificial Intelligence system is proposed over an offensive framework in gamification of WiFi environment. This work describes gamification of the WiFi environment for capturing those packets using Advantage-Actor-Critic based deep Reinforcement Learning method. Also, the game creature is deployed on a Raspberry pi model that learns from the network environment and makes it as a miniature pocket monster to facilitate the existing manual wireless packet capture process. These smart creatures can also communicate with one another by broadcasting their presence to each other and dividing Wi-Fi channels amongst themselves thereby setting up an internet of things.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, MANIT, Bhopal, 462003, India

    Vandana Shakya, Jaytrilok Choudhary & Dhirendra Pratap Singh

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  1. Vandana Shakya
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  2. Jaytrilok Choudhary
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  3. Dhirendra Pratap Singh
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Correspondence to Vandana Shakya.

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Shakya, V., Choudhary, J. & Singh, D.P. An advanced actor critic deep reinforcement learning technique for gamification of WiFi environment. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03582-4

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