yue123161

Anomaly-ReactionRL

Using RL for anomaly detection in NSL-KDD

dagmm

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

AppIdentificationHTTPS

论文"Mobile Application Identification Over HTTPS Traffic Based on Multi-view Features"程序

application_classification

To identify pkts or flow progressively.

APTSimulator

A toolset to make a system look as if it was the victim of an APT attack

botnet-flow-analysis

classification-of-encrypted-traffic

This repository contains the code used and developed during a master thesis at DTU Compute in 2018

DAZER

The Tensorflow implementation of accepted ACL 2018 paper "A deep relevance model for zero-shot document filtering", Chenliang Li, Wei Zhou, Feng Ji, Yu Duan, Haiqing Chen, http://aclweb.org/anthology/P18-1214

deep-learning-books

DeepTraffic

Deep Learning models for network traffic classification

fanci

FANCI is a prototype implementation of a machine learning based classification engine for non-existent domains to detect domain gernation algorithm malware traffic.

homoglyph

ImprovedIUPTIS

Publication 'Realistically Fingerprinting Social Media Webpages in HTTPS Traffic'

IoT_Sentinel

IoT SENTINEL : Automated Device-Type Identification for Security Enforcement in IoT https://arxiv.org/pdf/1611.04880.pdf

IOTAnomaly

Anomaly Detection in Network Traffic of IOT Devices

kdd99_feature_extractor

Utility for extraction of subset of KDD '99 features from realtime network traffic or .pcap file

KitNET-py

KitNET is a lightweight online anomaly detection algorithm, which uses an ensemble of autoencoders.

malware-prediction-rnn

RNN implementation with Keras for machine activity data to predict malware

MDAN

Demo code for the MDAN paper.

MPTAnalysis

Code for USENIX'18 paper: "Effective Detection of Multimedia Protocol Tunneling using Machine Learning"

MultimodalAutoencoder

Code supporting the paper "Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood Prediction"

MultitaskTrafficClassification

Multitask Learning Approach for Network Traffic Classification

outlier-exposure

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

ransomware-detection-with-deep-learning

Detecting ransomware delivery with DNNs

Semi-supervised-Learning-QUIC-

Implementation of "How to Achieve High Classification Accuracy with Just a Few Labels: A Semi-supervised Approach Using Sampled Packets"

Traffic-Classification

Malicious traffic classification by Convolutional Neural Network

translearn

Code implementation of the paper "With Great Training Comes Great Vulnerability: Practical Attacks against Transfer Learning", at USENIX Security 2018

URLNet

URLNet

WatermarkNN

Watermarking Deep Neural Networks (USENIX 2018)

website-fingerprinting

Deanonymizing Tor or VPN users with website fingerprinting and machine learning.