Computer network monitoring systems are crucial for gathering evidence in cases of computer fraud. This research introduces a tool called Sniffing Attack Prevention (SNAP), utilizing Sockets to connect a Client Node and Server Node for scanning open and closed ports on a wireless network. Developed in Python 3 with Scapy, SNAP identifies open ports, and detects and blocks sniffing attacks. The experimental results of SNAP demonstrate its ability to scan for various network parameters and successfully blocked an attack on 500 IP addresses and 200 ports within 2 min, where ports 135 and 139 were opened and 198 ports were closed before the sniffing attack was detected. With additional libraries for enhanced functionality, the tool is highly recommended for network administrators and cyber security specialists to secure wired and wireless networks. Future research is suggested for implementing a machine-learning approach to improve sniffing attack detection and prevention.