Amber

•    C

---

Amber - a quot;grey listquot; for Qmail. Amber sits in the tcpserver chain, not accepting or rejecting mail but deferring connections from new IP addresses for some time (default five minutes) before it starts accepting mail from them.

EmojiIntelligence - Neural Network built in Apple Playground using Swift

•    Swift

---

I used this challenge to learn more about neural networks and machine learning. A neural network consists of layers, and each layer has neurons. My network has three layers: an input layer, a hidden layer, and an output layer. The input to my network has 64 binary numbers. These inputs are connected to the neurons in the hidden layer. The hidden layer performs some computation and passes the result to the output layer neuron out. This also performs a computation and then outputs a 0 or a 1. The input layer doesn’t actually do anything, they are just placeholders for the input value. Only the neurons in the hidden layer and the output layer perform computations. The neurons from the input layer are connected to the neurons in the hidden layer. Likewise, both neurons from the hidden layer are connected to the output layer. These kinds of layers are called fully-connected because every neuron is connected to every neuron in the next layer. Each connection between two neurons has a weight, which is just a number. These weights form the brain of my network. For the activation function in my network, I use the sigmoid function. Sigmoid is a mathematical function. The sigmoid takes in some number x and converts it into a value between 0 and 1. That is ideal for my purposes, since I am dealing with binary numbers. This will turn a linear equation into something that is non-linear. This is important because without this, the network wouldn’t be able to learn any interesting things. I have already mentioned that the input to this network are 64 binary numbers. I resize the drawn image to 8x8 pixels which makes together 64 pixels. I go through the image and check each pixel if the pixel has a pink color I add a 1 to my array else I add a 0. At the end I will have 64 binary numbers which I can add to my input layer.

neural network apple playground ipad ai machine-learning artificial-intelligence

trigger - Trigger is a robust network automation toolkit written in Python that was designed for interfacing with network devices

•    Python

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Trigger is a robust network automation toolkit written in Python that was designed for interfacing with network devices and managing network configuration and security policy. It increases the speed and efficiency of managing large-scale networks while reducing the risk of human error. Started by the AOL Network Security team in 2006, Trigger was originally designed for security policy management on firewalls, routers, and switches. It has since been expanded to be a full-featured network automation toolkit.

network-automation systems networking networking-programmability network-engineers acls

LibreNMS - Network monitoring system

•    PHP

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LibreNMS is an autodiscovering PHP/MySQL/SNMP based network monitoring which includes support for a wide range of network hardware and operating systems including Cisco, Linux, FreeBSD, Juniper, Brocade, Foundry, HP and many more.

network monitoring rrd librenms network-monitoring

Network Security Toolkit (NST)

•    Javascript

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Network Security Toolkit (NST) is a bootable ISO image (Live DVD) based on Fedora 18 providing easy access to best-of-breed Open Source Network Security Applications and should run on most x86/x86_64 platforms. The main intent of developing this toolkit was to provide the network security administrator with a comprehensive set of Open Source Network Security Tools. The majority of tools published in the article: Top 125 Security Tools by INSECURE.ORG are available in the toolkit. An advanc

bitnodes - Bitnodes is currently being developed to estimate the size of the Bitcoin network by finding all the reachable nodes in the network ·

•    Python

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Bitnodes is currently being developed to estimate the size of the Bitcoin network by finding all the reachable nodes in the network. The current methodology involves sending getaddr messages recursively to find all the reachable nodes in the network, starting from a set of seed nodes. Bitnodes uses Bitcoin protocol version 70001 (i.e. >= /Satoshi:0.8.x/), so nodes running an older protocol version will be skipped.See Provisioning Bitcoin Network Crawler for steps on setting up a machine to run Bitnodes. The Redis Data contains the list of keys and their associated values that are written by the scripts in this project. If you wish to access the data, e.g. network snapshots, collected using this project, see Bitnodes API v1.0.

bitcoin

brain - [UNMAINTAINED] Simple feed-forward neural network in JavaScript

•    Javascript

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Download the latest brain.js. Training is computationally expensive, so you should try to train the network offline (or on a Worker) and use the toFunction() or toJSON() options to plug the pre-trained network in to your website. Use train() to train the network with an array of training data. The network has to be trained with all the data in bulk in one call to train(). The more training patterns, the longer it will probably take to train, but the better the network will be at classifiying new patterns.

neural-network classifier machine-learning

retina-unet - Retina blood vessel segmentation with a convolutional neural network

•    Python

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This repository contains the implementation of a convolutional neural network used to segment blood vessels in retina fundus images. This is a binary classification task: the neural network predicts if each pixel in the fundus image is either a vessel or not. The neural network structure is derived from the U-Net architecture, described in this paper. The performance of this neural network is tested on the DRIVE database, and it achieves the best score in terms of area under the ROC curve in comparison to the other methods published so far. Also on the STARE datasets, this method reports one of the best performances. The training of the neural network is performed on sub-images (patches) of the pre-processed full images. Each patch, of dimension 48x48, is obtained by randomly selecting its center inside the full image. Also the patches partially or completely outside the Field Of View (FOV) are selected, in this way the neural network learns how to discriminate the FOV border from blood vessels. A set of 190000 patches is obtained by randomly extracting 9500 patches in each of the 20 DRIVE training images. Although the patches overlap, i.e. different patches may contain same part of the original images, no further data augmentation is performed. The first 90% of the dataset is used for training (171000 patches), while the last 10% is used for validation (19000 patches).

Divert - Windows Packet Divert

•    C

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Windows Packet Divert (WinDivert) is a user-mode packet interception library for Windows 7, Windows 8 and Windows 10. WinDivert enables user-mode capturing/modifying/dropping of network packets sent to/from the Windows network stack. In summary, WinDivert can Capture network packets, Filter/drop network packets, Sniff network packets, (re)inject network packets, modify network packets. It can be used to implement user-mode packet filters, sniffers, firewalls, NATs, VPNs, IDSs, tunneling applications, etc.

packet-capture packet-sniffer firewall divert-sockets packet-interception sniffing

Mininet - An Instant Virtual Network on your Laptop (or other PC)

•    Python

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Mininet emulates a complete network of hosts, links, and switches on a single machine. It creates a realistic virtual network, running real kernel, switch and application code, on a single machine (VM, cloud or native), in seconds, with a single command.

sdn software-defined-network virtual-networking emulator simulator

PJON - PJON® is an open-source network protocol compatible with Arduino, ESP8266, STM32, Teensy, Raspberry Pi, Linux and Windows

•    C++

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PJON® (Padded Jittering Operative Network) is an Arduino compatible, multi-master, multi-media network protocol. It proposes a Standard, it is designed as a framework and implements a totally software emulated network protocol stack that can be easily cross-compiled on many architectures like ATtiny, ATmega, ESP8266, ESP32, STM32, Teensy, Raspberry Pi, Linux, Windows x86 and Apple machines. It is a valid tool to quickly and comprehensibly build a network of devices. Visit wiki and documentation to know more about the PJON Standard. Feel free to send a pull request sharing something you have made that could help. If you want to support us you can also try to solve an issue. Thank you for your support.

communication-protocol communication-library iot protocol-specification multi-master framework open-source arduino attiny85 esp8266 teensy raspberry-pi network-protocol network hc-12 internet-of-things decentralization privacy

DeepNeuralClassifier - Deep neural network using rectified linear units to classify hand written symbols from the MNIST dataset

•    Julia

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Example scripts for a deep, feed-forward neural network have been written from scratch. No machine learning packages are used, providing an example of how to implement the underlying algorithms of an artificial neural network. The code is written in the Julia, a programming language with a syntax similar to Matlab. The neural network is trained on the MNIST dataset of handwritten digits. On the test dataset, the neural network correctly classifies 98.42 % of the handwritten digits. The results are pretty good for a fully connected neural network that does not contain a priori knowledge about the geometric invariances of the dataset like a Convolutional Neural Network would.

awesome-network-analysis - A curated list of awesome network analysis resources.

•    R

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An awesome list of resources to construct, analyze and visualize network data. Inspired by Awesome Deep Learning, Awesome Math and others.

network-analysis network-visualization complex-networks political-networks semantic-networks graph-theory disease-networks network-science social-networks social-network-analysis historical-networks sna awesome-list awesome list

ReactiveNetwork - Android library listening network connection state and Internet connectivity with RxJava Observables

•    Java

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ReactiveNetwork is an Android library listening network connection state and Internet connectivity with RxJava Observables. It's a successor of Network Events library rewritten with Reactive Programming approach. Library supports both new and legacy network monitoring strategies. Min sdk version = 9. Please note: Due to memory leak in WifiManager reported in issue 43945 in Android issue tracker it's recommended to use Application Context instead of Activity Context.

android internet-connection network-connection network-monitoring wifi network internet rxjava rxjava2 rxandroid rxandroid2

Weave - Simple, Resilient Multi-host Docker Networking

•    Go

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Weave is a simple, portable and reliable way to network and manage containers and microservices. It provides a simple and resilient network for your application that is portable across data centers and public clouds. Weave Net creates a virtual network that connects Docker containers across multiple hosts and enables their automatic discovery.

virtual-network cloud docker kubernetes container-networking

NFStream - A Flexible Network Data Analysis Framework

•    Python

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NFStream is a Python package providing fast, flexible, and expressive data structures designed to make working with online or offline network data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world network data analysis in Python. Additionally, it has the broader goal of becoming a common network data processing framework for researchers providing data reproducibility across experiments. NFStream extracts +90 flow features and can convert it directly to a pandas Dataframe or a CSV file.

data-science data-analysis data-mining network-analysis network-security network-monitoring cybersecurity machine-learning artificial-intelligence dataset-generation deep-packet-inspection netflow traffic-analysis traffic-classification pcap packet-capture packet-analyser ndpi

Social Network Importer for NodeXL

•    

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The Social Network Importer for NodeXL imports network data from Facebook. Users can download their own facebook ego network or the connection on Fan pages.

facebook facebook-importer facebook-network facebook-spigot graph network nodexl

lnd - Lightning Network Daemon

•    Go

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The Lightning Network Daemon (lnd) - is a complete implementation of a Lightning Network node and currently deployed on testnet3 - the Bitcoin Test Network. lnd has several pluggable back-end chain services including btcd (a full-node) and neutrino (a new experimental light client). The project's codebase uses the btcsuite set of Bitcoin libraries, and also exports a large set of isolated re-usable Lightning Network related libraries within it.

bitcoin lightning-network blockchain micropayments lightning protocol cryptography peer-to-peer payments

Peep: The Network Auralizer

•    C

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Peep is a network monitoring tool that represents network information via an audio interface. Network diagnostics are made not only based on single network events but whether the network sounds quot;normalquot;.

zarp - Network Attack Tool

•    Python

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Zarp is a network attack tool centered around the exploitation of local networks. This does not include system exploitation, but rather abusing networking protocols and stacks to take over, infiltrate, and knock out. Sessions can be managed to quickly poison and sniff multiple systems at once, dumping sensitive information automatically or to the attacker directly. Various sniffers are included to automatically parse usernames and passwords from various protocols, as well as view HTTP traffic and more. DoS attacks are included to knock out various systems and applications. These tools open up the possibility for very complex attack scenarios on live networks quickly, cleanly, and quietly. The long-term goal of zarp is to become the master command center of a network; to provide a modular, well-defined framework that provides a powerful overview and in-depth analysis of an entire network. This will come to light with the future inclusion of a web application front-end, which acts as the television screen, whereas the CLI interface will be the remote. This will provide network topology reports, host relationships, and more. zarp aims to be your window into the potential exploitability of a network and its hosts, not an exploitation platform itself; it is the manipulation of relationships and trust felt within local intranets. Look for zeb, the web-app frontend to zarp, sometime in the future.