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Attack Surface Management | Criminal IP ASM
For security operations, threat-intelligence and risk teams wanting a tool to get access to auto-monitored assets exposed to attack surfaces
Criminal IP’s Attack Surface Management (ASM) is a threat-intelligence–driven platform that continuously discovers, inventories, and monitors every internet-connected asset associated with an organization, including shadow and forgotten resources, so teams see their true external footprint from an attacker’s perspective. The solution combines automated asset discovery with OSINT techniques, AI enrichment and advanced threat intelligence to surface exposed hosts, domains, cloud services, IoT endpoints and other Internet-facing vectors, capture evidence (screenshots and metadata), and correlate findings to known exploitability and attacker tradecraft. ASM prioritizes exposures by business context and risk, highlights vulnerable components and misconfigurations, and provides real-time alerts and dashboards to speed investigation and remediation.
By using a neuralnet from neuroph.org with the programmlanguage Java, you can change the net by this software.
Easy watch it on the screen and move the values up or down. More or less value at the output neurons.
Saving, Loading from different .nnet`s.
Adding comments.
Changing the visible output neuron
Setting wished input neurons to get visible.
Topographica is a neural modeling package developed in a Human Brain Project grant from NIH. Topographica helps neuroscientists and computational scientists simulate and understand how topographic maps contribute to brain function. Topographica is now primarily maintained at github.com; see https://github.com/ioam/topographica for recent updates and releases.
Open Bayes is a python free/open library that allows users to easily create a bayesian network and perform inference/learning on it. It is mainly inspired from the Bayes Net Toolbox (BNT) but uses python as a base language. www.openbayes.org
UPDATE:
The latest version of NNScore1 can be found here: http://git.durrantlab.com/jdurrant/nnscore1
The latest version of NNScore2 can be found here: http://git.durrantlab.com/jdurrant/nnscore2
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NNScore is a scoring function for characterizing the potency of receptor-ligand complexes. It is based on neural networks, computational models that simulate the microscopic organization of the brain.
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This is a c-library that provides tools for advanced
analysis of electrophysiological data. It features
denoising, unsupervised classification, time-frequency
analysis, phase-space analysis, neural networks, time-warping and
more.
This toolkit allows complete control of a microscopy setup from Labview, Matlab, Scilab, Python, .Net, VB, IgorPro, Mathematica and more. Included is a standalone program for image acquisition and scripting control of a scientific microscope.
A three-step approach towards experimental brain-computer-interfaces, based on the OCZ nia device for EEG-data acquisition and artificial neural networks for signal-interpretation.
Brian is a new simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists.
PetriKit is a modular toolkit for Petri Net analysis. It allows the extraction of basic properties, extraction of invariants, and generation of reachability graph. Results can be formatted in plain text, HTML or XML files.
Silverware is built for hotel, resort, and multi-venue hospitality operators who need enterprise-grade control, deep integrations, and always-on reliability to run complex operations at scale.
The aim of GUINNEA (Graphical User Interfaced Neural Network Architecture) is to develop a comfortable and high-featured neuralnet simulator which is highly configurable and flexible. It will support many neural nets and visualization features for those
Dimenso is a library of numeric types for .Net and Java, that obey dimensional analysis and permit calculations with physical quantities. Due to the lack of C++ - like templates with numeric arguments, code generation is employed using Python.
We experiment with Evolution of Artifical Neural Networks, combining the two fields of Evolutionary Computation and ANNs. Our methods are applied to a variety of interesting problems. To learn more, click on "Home Page", "Mail", or "Files".
NGmp is a C# C++ Java Python VB.NET etc. etc. binding of GMP and MPFR multi-precision number libraries. With support of Microsoft .NET or Novell Mono runtime NGmp will run on mutliple platforms. It is faster than other libraries available on .NET or Mono
Jannis stands for "J Artificial NeuralNet Interactive Simulator".
Jannis' primary purpose is simulation of neuralnet models for scientific experiments in special-needs-education and neuropsychology.
neuralnetgen is intended to be a universal neuralnet generator that receives a neuralnet description (on NeuralNet Description Language, also developed on this project) and generates ANSI C code of a library that implements it.