Stars
Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15
Network Intrusion Detection based on various machine learning and deep learning algorithms using UNSW-NB15 Dataset
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
(TensorFlow-Keras) implementation of most of the interesting deep learning papers (based on my field of interest).
This projected explored the effect of introducing channel and spatial attention mechanisms, namely SEN-Net, ECA-Net, and CBAM to existing CNN vision-based models such as VGGNet, ResNet, and ResNetV…
This repo contains the 3D implementation of the commonly used attention mechanism for imaging.
CBAM implementation on TensorFlow Slim
Keras Attention Layer (Luong and Bahdanau scores).
Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets
Memory consumption and FLOP count estimates for convnets
Tensorflow-Keras Model Profiler: Tells you model's memory requirement, no. of parameters, flops etc.
Scaled-YOLOv4: Scaling Cross Stage Partial Network
This repository contains the source code of our work on designing efficient CNNs for computer vision
Dimension-Aware Attention for Efficient Mobile Networks
This is a function for estimating the floating point operations (FLOPS) of deep learning models developed with keras.
ML-Ensemble – high performance ensemble learning
Cyber Attack Detection thanks to Machine Learning Algorithms
Evaluating multiple classifiers after SVM-RFE (Support Vector Machine-Recursive Feature Elimination)
A game theoretic approach to explain the output of any machine learning model.
A Behavior-Based Device Identification Method for the IoT
How to create a confusion matrix with the test result in your training model & How to visualize the history of network learning: accuracy, loss in graphs.
In classification, Accuracy, Precision, Recall, F1, Confusion Matrix, ROC, AUC are the important Evaluation Indicator
Deep learning simplified by transferring prior learning using the Python deep learning ecosystem
This is for Anomlay detection on UNSW-NB15
Machine Learning for Intrusion Detection using the UNSW-NB15 dataset