Pinned Repositories
AI-Driven-WAF
Artificial intelligence-driven Web Firewall
CNN-Adversarial-Examples-on-CSIC-2010
CNNVD
Fault-prediction-using-NASA-MDP-Dataset
Identifying the best algorithm for defect prediction in softwares and providing the most accurate results. The Metrics Data Program dataset provided by NASA has been used.
Feature-Selection-Hybrid
Intrusion Detection is a technique to identify the abnormal behavior of system due to attack. The unusual behavior of the environment is then identified and steps are taken and methods are formed to classify and recognize attacks. Data set containing a number of records sometimes may decrease the classifiers performance due to redundancy of data. The other problems may include memory requirements and processing power so we need to either reduce the number of data or the number of records. Feature Selection techniques are used to reduce the vertical largeness of data set. This project makes a comparative study of Particle Swarm Optimization, Genetic Algorithm and a hybrid of the two where we see that PSO being simpler swarm algorithm works for feature selection problems but since it is problem dependent and more over its stochastic approach makes it less efficient in terms of error reduction compared to GA. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on sub optimal solutions that are not even guaranteed to be local optimum. A further drawback is that stochastic approaches have problem-dependent performance. This dependency usually results from the parameter settings in each algorithm. The different parameter settings for a stochastic search algorithm result in high performance variances. In this project the modification strategies are proposed in PSO using GA. Experimental results show that GA performs better than PSO for the feature selection in terms of error reduction problems whereas hybrid outperforms both the model in terms of error reduction.
GANs-for-imbalanced-data-generation
Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
KNN-Algorithm-KDD-99-Dataset
Machine-Learning-on-CSIC-2010
Machine-Learning-on-CSIC-2011
Machine Learning on dataset HTTP CSIC 2010
wangyu521's Repositories
wangyu521/ssa-lstm
wangyu521/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
wangyu521/KNN-Algorithm-KDD-99-Dataset
wangyu521/vulndetect-ml
Simple ML project to detect and classify vulnerable Java code
wangyu521/Machine-Learning-on-CSIC-2010
wangyu521/Fault-prediction-using-NASA-MDP-Dataset
Identifying the best algorithm for defect prediction in softwares and providing the most accurate results. The Metrics Data Program dataset provided by NASA has been used.
wangyu521/CNN-Adversarial-Examples-on-CSIC-2010
wangyu521/CNNVD
wangyu521/Network-Intrusin-Detection-Using-Fuzzy-clustering-and-ANN
A simple classification model on famous network dataset-NSL KDD
wangyu521/mdp_classify
NASA MDP 软件缺陷数据集分类
wangyu521/GANs-for-imbalanced-data-generation
wangyu521/Vulnerability_classify
NVD，CNNVD软件漏洞数据集，漏洞文本预处理，训练算法模型进行漏洞分类
wangyu521/Machine-Learning-on-CSIC-2011
Machine Learning on dataset HTTP CSIC 2010
wangyu521/software-vulnerabilities-analysis
Exploratory analysis and application of machine learning techniques to predict whether a software is vulnerable or not. For Data Mining and Exploration classes.
wangyu521/Using-machine-learning-to-detect-malicious-URLs
Machine Learning and Security | Using machine learning to detect malicious URLs
wangyu521/SoftwareVulnerabilityAnalysisByMachineLearning
Software Vulnerability Analysis By Machine Learning
wangyu521/Feature-Selection-Hybrid
Intrusion Detection is a technique to identify the abnormal behavior of system due to attack. The unusual behavior of the environment is then identified and steps are taken and methods are formed to classify and recognize attacks. Data set containing a number of records sometimes may decrease the classifiers performance due to redundancy of data. The other problems may include memory requirements and processing power so we need to either reduce the number of data or the number of records. Feature Selection techniques are used to reduce the vertical largeness of data set. This project makes a comparative study of Particle Swarm Optimization, Genetic Algorithm and a hybrid of the two where we see that PSO being simpler swarm algorithm works for feature selection problems but since it is problem dependent and more over its stochastic approach makes it less efficient in terms of error reduction compared to GA. In standard PSO, the non-oscillatory route can quickly cause a particle to stagnate and also it may prematurely converge on sub optimal solutions that are not even guaranteed to be local optimum. A further drawback is that stochastic approaches have problem-dependent performance. This dependency usually results from the parameter settings in each algorithm. The different parameter settings for a stochastic search algorithm result in high performance variances. In this project the modification strategies are proposed in PSO using GA. Experimental results show that GA performs better than PSO for the feature selection in terms of error reduction problems whereas hybrid outperforms both the model in terms of error reduction.
wangyu521/UrlDetect
a demo for detecting anomaly url
wangyu521/AI-Driven-WAF
Artificial intelligence-driven Web Firewall
wangyu521/VulnerabilitiesClassifier
This project helps to classify the different vulnerabilities reported until today. It takes as input source an XML file according to the schema provided by NIST. The returned output is a file in csv format.
wangyu521/NASADefectDataset
NASA Cleaned Defect Datasets