Originyeah

ember

Elastic Malware Benchmark for Empowering Researchers

MyMasterLearning

一些光谱与图像的读取，处理，和算法

Fraud_Detection

Tuning XGBoost hyper-parameters with Simulated Annealing

XGBoost_Hyperparameter_Tuning_with_Simulated_Annealing

Hyperparameter optimization of XGBoost using a metaheuristic algorithm, Simulated Annealing (SA) applied on Financial Fraudulent case.

malware-detection

Malware Detection and Classification Using Machine Learning

DeepMalwareDetector

A Deep Learning framework that analyses Windows PE files to detect malicious Softwares.

svm-ddos-sdn

DDoS attacks detection based on SVM and mitigation in a Software-Defined Network.

ddosdn

Mitigation and Detection of DDoS Attacks in Software Defined Networks

project

DDoS attacks detection by using SVM on SDN networks.

sdn-network-ddos-detection-using-machine-learning

sdn network ddos detection using machine learning

Anomaly-detection-system

Machine learning based Intrusion detection system (IDS)

PBCNN

PyDataPreprocessing

《Python数据预处理技术与实践》源码下载

Network-Intrusion-Detection

Network Intrusion Detection KDDCup '99', NSL-KDD and UNSW-NB15

dataset_UNSWNB15

The dataset that has been processed and the code used to process it

ML-Projects

Basic Machine learning projects and assignments done by me.

UNSW_NB15

Feature coded UNSW_NB15 intrusion detection data.

SVM

支持向量机，Support Vector Machine(SVM)，多类分类

svm

用Python实现SVM多分类器

Feature-Selection-By-BSTA

特征选择建模成单目标优化问题，目标函数是特征子集在学习器（SVM）上的分类准确率，并将解（特征子集）用0，1编码，即 0 代表该特征不被选中，1代表被选中。因此，特征选择问题变成了一个离散二值优化问题，然后使用离散状态转移算法来做特征选择

DescentAlgorithmBenchmark

Benchmarking of various descension algorithms on Dixon-Price test function

evo-svm

Evolutionary Support Vector Machine

Classification-Optimization-technique

Fraud detection over twitter feed data

CPP-in-SDN

MATLAB simulations for Controller Placement Problem in Software Defined Networks

malware-detection

Implementation of a Dynamic Hybrid ANFIS-Whales approach for malware detection using generated datasets.

Metaheuristic-Optimizer-for-Feature-Selection---PSO

The following gives information regarding the Particle Swarm Optimizer for feature selection process using various machine learning classifiers within the classification function

Optimal-Feature-selection-for-KNN-classifier

This MATLAB code implements the binary Grass hopper optimization algorithm to select the features and train with KNN

Equilibrium-Optimizer-for-Feature-Selection

Application of Equilibrium Optimizer (EO) in the feature selection tasks.

Particle-Swarm-Optimization-for-Feature-Selection

Application of Particle Swarm Optimization (PSO) in the feature selection tasks.

Whale-Optimization-Algorithm-for-Feature-Selection

Application of Whale Optimization Algorithm (WOA) in the feature selection tasks.