372 skills found · Page 5 of 13
SecurityInnovation / HolodeckHolodeck is a Fault Injection tool for testing Windows binaries and .NET applications. Holodeck utilizes fault-injection techniques to introduce the application to simulated scenarios that arise as the result of "broken" environments, such as out of memory conditions, corrupt files, bad registry data, or corrupted network packets.
team-approx-bayes / BayesBiNNCode for the paper "Training Binary Neural Networks with Bayesian Learning Rule
xwzy / Triplet Deep Hash PytorchPytorch implementation of "Fast Training of Triplet-based Deep Binary Embedding Networks".
lucamocerino / Binary Neural Networks PyTorch 2.xBNNs (XNOR, BNN and DoReFa) implementation for PyTorch 2.x+
mbahri / Binary GnnCode for our paper "Binary Graph Neural Networks", CVPR 2021
hpi-xnor / BitorchBITorch: Open-Source Implementation of Binary Neural Networks with PyTorch
vjoel / Bit StructLibrary for packed binary data stored in ruby Strings. Useful for accessing fields in network packets and binary files.
Wzixin / SKD BNNThe pytorch implementation of our paper : Self-Knowledge Distillation enhanced Binary Neural Networks using Underutilized Information.
neuluis / Hcaptcha SolverAutomated hCaptcha solver using binary image classification networks
archienorman11 / Thesis Bitcoin ClusteringThe Bitcoin currency is a publicly available, transparent, large scale network in which every single transaction can be analysed. Multiple tools are used to extract binary information, pre-process data and train machine learning models from the decentralised blockchain. As Bitcoin popularity increases both with consumers and businesses alike, this paper looks at the threat to privacy faced by users through commercial adoption by deriving user attributes, transaction properties and inherent idioms of the network. We define the Bitcoin network protocol, describe heuristics for clustering, mine the web for publicly available user information and finally train supervised learning models. We show that two machine learning algorithms perform successfully in clustering the Bitcoin transactions based on only graphical metrics measured from the transaction network. The Logistic Regression algorithm achieves an F1 score of 0.731 and the Support Vector Machines achieves an F1 score of 0.727. This work demonstrates the value of machine learning and network analysis for business intelligence; on the other hand it also reveals the potential threats to user privacy.
brais-martinez / Real2binaryCode for the ICLR2020 "Training Binary Neural Networks with Real-to-Binary Convolutions
stendec / NetstructPython packed binary data for networking.
yikaiw / SNN[ICCV 2021] Code release for "Sub-bit Neural Networks: Learning to Compress and Accelerate Binary Neural Networks"
saucermanlab / NetfluxNetflux is a user-friendly software for developing dynamic computational models of biological networks. Models are created in Excel format and then simulated using the Netflux graphical interface. No computer programming is required. Netflux is written in MATLAB, with binary versions available for Windows and MacOS.
renproject / PackAn application binary interface for well-typed message passing over networks and disks
XinDongol / BENN PyTorchCodes for Binary Ensemble Neural Network: More Bits per Network or More Networks per Bit?
Meoqan / VaserVaser is a powerful high performance event based network engine library for C# .Net. It’s possible to start multiple servers in one program and use the same network code for all servers. In the network communication are all strings are omitted, instead it is based on a unique binary identifier, which the CPU and memory relieves massively.
cleesmith / UnifiedbeatUnifiedbeat reads records from Unified2 binary files generated by network intrusion detection software and indexes the records in Elasticsearch.
Hashehri / Network Traffic Classification UNSW NB15Binary Classification for detecting intrusion network attacks. In order, to emphasize how a network packet with certain features may have the potentials to become a serious threat to the network.
srtk88 / Machine Learning Algorithms For Detecting Network Attacks With UNSW NB15 Data SetDue to the increasingly development of network technology recently, there are various cyber-attacks posed the huge threats to different fields around the world. Many studies and researches about cyber-security are carried out by experts in order to construct a safe network environment for people. The aim of the work is to build the detection models for classifying the attack data. Hence, we applied the UNSW-NB15 network data set which combines both normal and modern low-level attacks because we would like to create the experimental scenario close to the real world. Two classifiers are logistic regression and decision tree model for binary classification in the work. The deployed technique for decision tree achieved the highest result with 99.99% of testing accuracy compare to the 78.15% of logistic regression classifier. On the other hand, the KNN model is used for categorizing the multi-class in the project, and the averaged accuracy for testing is around 23% for ten categories classification.
