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a-labelled-version-of-the-adfa-ld-dataset

a labelled version of the ADFA-LD dataset for HIDS evaluation

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

anomaly-detection-using-cgan-and-lstm-autoencoder

Fake Data + Real Data = CGAN

anomaly_detection

MD,LSTM-AE,VAE-MAD-GAN

awesome-ml-for-cybersecurity

:octocat: Machine Learning for Cyber Security

behavioar-based-insider-threat-detection-using-deep-learning

Cyber security issues are around the globe where data security is the major concern, one or the another company vulnerable to data leakage issues by the insiders , So to overcome this insider threats issues we developed a model which detects the insider attack prior. In this we have used LSTM-CNN and BI-LSTM-CNN to deploy a model.

bigan-for-host-behavior-anomaly-detection

BiGAN for Host Behavior Anomaly Detection

deepad

Deep Learning for Anomaly Deteection

deepadots

Repository of the paper "A Systematic Evaluation of Deep Anomaly Detection Methods for Time Series".

deeptraffic

Deep Learning models for network traffic classification

dqn-timeseries-anomaly-detection

This repository focuses on anomaly detection with Reinforcement Learning. DQNs are used to approximate the value function for anomalies on timeseries. The repository is part of my Master Thesis in Computer Science

efficientdet

(Pretrained weights provided) EfficientDet: Scalable and Efficient Object Detection implementation by Signatrix GmbH

gan_review

A Survey and Taxonomy of the Recent GANs Development，computer vision & time series

gym-fraud

💳 Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

insdetek.insider_threat_detection_system

[CrowdStrike] Security Product to Identify high risk user activity when interacting with managed hosts, business applications and data

insider-threat

insider-threat-detection

An insider threat detection system

insider-threat-detection-using-ml

insider-threats-detection

Developed a deep learning model using Convolutional Neural Network(CNN) to predict possible insider threats in an organization with data set contains human brain waves using python deep learning libraries(Keras) that achieved 96% accuracy.

insider_threat_risk_prediction

NYCDSA - Capstone Project

insiderthreat

intrusion-and-anomaly-detection-with-machine-learning

Machine learning algorithms applied on log analysis to detect intrusions and suspicious activities.

mad-gans

Applied generative adversarial networks (GANs) to do anomaly detection for time series data

ml-cybersecurity

Machine Learning in Cybersecurity

network-data-generation-vanilla-gan

A generative deep learning model based on GAN architecture was implemented to generate synthetic network data (benign and malicious) alike that within the CIC-IDS-2017 dataset.

network-intrusion-detection-using-machine-learning

A Novel Statistical Analysis and Autoencoder Driven Intelligent Intrusion Detection Approach

networkintrusiondetection

This project uses various deep learning models, such as FFNN, LSTM, and ESN to enhance the detection of denial of service attacks on the CSE-CIC-IDS2018 datasets.

prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

seqgan

Implementation of Sequence Generative Adversarial Nets with Policy Gradient

sgm-cnn

Our implementations of the flow-based network intrusion detection model (for the COMNET paper)