k8spacket - collects TCP traffic and TLS connection metadata in the Kubernetes cluster using eBPF and visualizes in Grafana
A packet sniffer (also known as an IP sniffer/puller) specifically designed for Peer-To-Peer (P2P) video games on PC and consoles (PlayStation and Xbox).
Packet injection for wifi; simplified.
Free Wireshark tutorials for beginners with 13 interactive lessons. Easy-to-follow programming guides with hands-on practice exercises.
Python Tools
(2017) A WoW 1.12.2 Packet Manipulation / Multihack tool.
a proof of concept ESP32C6 sniffer
On the fly encryption and decryption for WEP and WPA
and yet another proof of concept sniffer using the IEEE 802.15.4 standard
Bleeding edge work for airpwn-ng
Wireshark packet analysis for CoAP and MQTT traffic, plus IoT device energy consumption estimation using real measurement data.
WaveScan is a comprehensive web security scanner designed for administrators, developers, and security professionals to perform complete vulnerability assessments
A network packet sniffing tool written in Python 3
This is about socket communication and packet sniffing on the network. Development language is Python, and the driving test was performed through Linux.
A sniffing attack involves the illegal extraction of unencrypted data by capturing network traffic through packet sniffers. In this tool, the user will be given 4 options such as raw, summary, target, and host. The user can select any option and they can start sniffing the network. The user has to just enter the name of the interface.
Packet sniffer tool for network analysis.
(M)onitor (A)RP queries and (D)NS queries to (w)akeup (a) (S)erver
A windows application for listening packets in promiscous mode or over UDP [layer 4] and distribute over configured IPs.
A command line based tool for basic Network Analysis using Libtins a high-level, multiplatform C++ network packet sniffing and crafting library
An advanced cybersecurity system designed to monitor network traffic and system logs in real time, detect potential threats, and provide actionable insights. The project combines machine learning–based anomaly detection with rule-based techniques to identify suspicious activities such as unauthorized access and network attacks.
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