Detection and prevention of anomalies based on signal monitoring to protect against deauthentication attacks in wireless networks

The use of the internet has increased so much in today's world that, according to 2021 data, 59.5% of the world's population uses the internet. Wireless network technologies play a significant role in the widespread use of the internet. In this case also bring along security issues. There are numerous potential threats to communication security in wireless networks. Hackers can monitor, modify, and disclose the network traffic of users connected to wireless networks. Due to its ease of execution and its potential to open the door to many other security problems, deauthentication attacks are considered the most dangerous attack method. In this method, the attackers exploit security vulnerabilities in wireless network communication protocols to terminate the users' connections. After this stage, hackers can implement various attack techniques according to their objectives. The aim of this thesis is to develop a software to detect and prevent deauthentication attacks in wireless networks. In this software, the Python programming language, the Scapy library, and a user-friendly graphical user interface (GUI) are used. In the first stage, networks in the environment are scanned with the Wi-Fi receiver card. Afterwards, the network that is required to be protected from the detection and warning software is selected for protection. When an attack is carried out, the detection and warning software gives an audible alert. Upon receiving the alert, we disconnect our connection from the network to prevent ourselves deauthentication attacks, which exploit anomalies.