Offical implementation of "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series" (ICLR 2022)

Multivariate Time-Series Anomaly Detection with GNN.

Official implmentation of AAAI'23 paper 'Detecting Multivariate Time Series Anomalies with Zero Known Label'.

A tool for data preprocess on iTrust SWaT dataset.

A fork and successor of the Sulley Fuzzing Framework

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

[ICMLC 2018] A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection

Anomaly Detection for SWaT Dataset using Sequence-to-Sequence Neural Networks

This is the source code of "Intrusion Detection of Industrial Internet-of-Things Based on Reconstructed Graph Neural Networks"

In this work, we aim at developing a NIDS (Network Intrusion Detection System) that detects attacks targeting SCADA systems, in a concrete industrial used case scenario.

Deep learning models for network intrusion detection

Code for intrusion detection system (IDS) development using CNN models and transfer learning

Log-based Anomaly Detection with Deep Learning: How Far Are We? (ICSE 2022, Technical Track)

Intrusion Detection Based on Convolutional Neural Network with kdd99 data set

A Deep Learning Based Intrusion Detection System for IIoT Networks

This project aims to detect Intrusions with a network using deep learning. The network traffic data is converted to multi channel RGB images, that are passed through CNNs to detect features useful …

Deep Learning techniques can be implemented in the field of cybersecurity to handle the issues related to intrusion just as they have been successfully implemented in the areas such as computer vis…

Apply modern, deep learning techniques for anomaly detection to identify network intrusions.

Network Intrusion Detection System using Deep Learning Techniques

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This work aims at using different machine learning techniques in detecting anomalies (including hardware failures, sabotage and cyber-attacks) in SCADA water infrastructure.

An Intrusion Detection System based on Deep Belief Networks

Code for Paper : Efficient-CNN-BiLSTM-for-Network-IDS

CANET: An Effective CNN-Attention Model for Network Intrusion Detection

Machine Learning with the NSL-KDD dataset for Network Intrusion Detection

VGG-19 deep learning model trained using ISCX 2012 IDS Dataset

IDS monitors a network or systems for malicious activity and protects a computer network from unauthorized access from users,including perhaps insider.

Here, we use RNN to deal with the network intrusion problem. The UNSW-NB15 dataset is used.

Network intrusions classification using algorithms such as Support Vector Machine (SVM), Decision Tree, Naive Baye, K-Nearest Neighbor (KNN), Logistic Regression and Random Forest.