Pinned Repositories
--2-Intrusion-Detection-System-Using-Machine-Learning
Code for intrusion detection system development using machine learning algorithms (Decision tree, random forest, extra trees, XGBoost, stacking, k-means, Bayesian optimization..)
-docker-python
Kaggle Python docker image
-Intrusion-Detection-System-Using-CNN-and-Transfer-Learning
Code for intrusion detection system (IDS) development using CNN models and transfer learning
222-Efficient-CNN-BiLSTM-for-Network-IDS
222-Efficient-CNN-BiLSTM-for-Network-IDS
awesome-DeepLearning
深度学习入门课、资深课、特色课、学术案例、产业实践案例、深度学习知识百科及面试题库The course, case and knowledge of Deep Learning and AI
awesome-rnn
Recurrent Neural Network - A curated list of resources dedicated to RNN
bert
TensorFlow code and pre-trained models for BERT
CS-Notes
:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计
E-ResGAT--
The pytorch implementation of E-GraphSAGE and E-ResGAT, two solutions for intrusion detection.
FlyA2.github.io
FlyA2's Repositories
FlyA2/CICID-
Machine Learning in Cybersecurity
FlyA2/2019-Fence_GAN
Fence GAN: Towards Better Anomaly Detection
FlyA2/MStream-C-
Anomaly Detection on Time-Evolving Streams in Real-time. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.
FlyA2/Edge-Detect
Repository for IEEE CCNC'21 paper titled "Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network".
FlyA2/-
This is the repo of the research paper, "Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security".
FlyA2/1-
Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats. In this project we attempt at solving this problem by presenting two taxonomies
FlyA2/AnomalyDAE-11
AnomalyDAE (ICASSP2020)
FlyA2/LuNet-CNN-RNN-
Pytorch implementation of LuNet: A Deep Neural Network for Network Intrusion Detection
FlyA2/Network-Intrusion-Detection-
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
FlyA2/11-cnn-CICIDS
I have tried some of the machine learning and deep learning algorithm for IDS 2017 dataset. The link for the dataset is here: http://www.unb.ca/cic/datasets/ids-2017.html. By keeping Monday as the training set and rest of the csv files as testing set, I tried one class SVM and deep CNN model to check how it works. Here the Monday dataset contains only normal data and rest of the days contains both normal and attacked data. Also, from the same university (UNB) for the Tor and Non Tor dataset, I tried K-means clustering and Stacked LSTM models in order to check the classification of multiple labels.
FlyA2/char-models-15
Character level models for sentiment analysis