bowl-of-porrige

bowl-of-porrige's Stars

• KimiNewt/pyshark

  Python wrapper for tshark, allowing python packet parsing using wireshark dissectors

  Language:Python2.3k431

• ymirsky/Kitsune-py

  A network intrusion detection system based on incremental statistics (AfterImage) and an ensemble of autoencoders (KitNET)

  Language:Python32098

• Hannibal046/Awesome-LLM

  Awesome-LLM: a curated list of Large Language Model

  20.9k1.7k

• E0HYL/DroidSIFT

  https://bitbucket.org/muzhang/droidsift/src/master/

  Language:Smali4

• Thijsvanede/DeepCASE

  Original implementation and resources of DeepCASE as in the S&P '22 paper

  Language:Python9125

• Trusted-AI/adversarial-robustness-toolbox

  Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams

  Language:Python5k1.2k

• CactiLab/code-xNIDS

  source code for USENIX Security paper xNIDS

  Language:Python368

• annamalai-nr/drebin

  Drebin - NDSS 2014 Re-implementation

  Language:Python10547

• markvdw/mml-autumn-2022

  Materials for Autumn 2022 Mathematics for Machine Learning course.

  Language:HTML3323

• prakhar1989/awesome-courses

  :books: List of awesome university courses for learning Computer Science!

  58.6k8.2k

• yinboc/DGP

  Rethinking Knowledge Graph Propagation for Zero-Shot Learning, in CVPR 2019

  Language:Python31957

• ronanmmurphy/Knowledge-Graph-Embeddings-to-Implement-Explainability

  Knowledge Graph Embeddings (KGE) to implement Explainable Artificial Intelligence. As AI develops users must know how algorithms make their decisions, especially for hazardous tasks such as driverless cars. Knowledge graphs are an inherently understandable form of text-based data created as an interconnected network of information. These can be converted into KGE by transforming the unqiue entites in the graph to vector representations. With these, predictions were made for missing/incorrect links in the network and further explainations were made by plotting the clusters of the data. Knowledge graphs and their embedded models were researched and four of these KGE were created and tested by their ability to rank the correct links from a Covid-19 dataset. This dataset was extracted from research papers about the virus to retrieve information quicker. The model which was most accurate was used to implement knowledge graph completion and explainability of the dataset using visual and textual interpretations. A 29,000-word thesis was written to describe the work done through the researching, testing and interpreting of this project.

  Language:Jupyter Notebook111

• Trusted-AI/AIX360

  Interpretability and explainability of data and machine learning models

  Language:Python1.7k310

• marcotcr/lime

  Lime: Explaining the predictions of any machine learning classifier

  Language:JavaScript11.7k1.8k

• shap/shap

  A game theoretic approach to explain the output of any machine learning model.

  Language:Jupyter Notebook23.3k3.3k

• InesMartins31/iot-cves

  IoT CVEs as abnormal events to evaluate a real-time host-based IDS. https://doi.org/10.1016/j.future.2022.03.001

  Language:Dockerfile93

• c2dc/wsensing2021

  Classification of Denial of Service Attacks on Wi-Fi-based Unmanned Aerial Vehicle

  Language:Jupyter Notebook3

• aozturk/HashMap

  Basic HashMap (Hash Table) Implementation in C++

  Language:C++10543