algos security

1-Class SVM
1-SVM
7-NN
9 International Journal of Pure
a deep neural network
a deep neural network (DNN) model.
a DL approach
a one-layer CNN models
A White-Box Attack against MLP
AAE
Accuracy
ADA boost
ADABoost
ADABOOST ALGORITHM
Adaboost with Decision Tree
AdaboostM1
AE
AIS
algorithm
ALGORITHM 1: training procedures of SAE.
ALGORITHM 2: CART creation process
ALGORITHM 3: tree generation process.
AMAEID
an AutoEncoder model
an RNN with LSTM model
analysis of using Machine Learning Techniques for Intrusion Detection
ANN
Ant colony algorithm
AntMiner strategy
Apache Storm using SVM
Applied Mathematics Special Issue 109 PCA
artificial neural network
artificial neural network (ANN)
Artificial Neural Network (MLP)
Association rule mining
Association rules
Auto Encoder (AE)
Autoencoder
AUTO-ENCODER
Autoencoder neural network
Autoencoders
Autoenoders
backpropagation neural network (BPNN)
Bagging
Bayes Net
Bayes Network
Bayes tree models
Bayesian Classification
Bayesian Network
Bayesian network s
Bayesian Networks
BayesNet
Bays Net
BestFirst
BGSA-SVM
bidirectional long short-term memory with attention
BiGAN
BiLSTM
Bi-LSTM
BIRCH Hierarchical Clustering Algorithm
BLoCNet
BLSTM
BN
BOOSTING
BP
BPNN
BT
BYES NET
C4.5
C4.5 decision tree
C5 decision tree
C5.0
CANN
CART
CART-BN
CASMN
Cat boost
CatBoost
Centralized Blending
CF Tree
Chi-Logistic Regression classifer.
Chi-SVM classifer
Classification
Clustering
Clustering algorithm i.e. Artificial Immune Network
Clustering techniques
CNN
CNN((Binary classification
CNN-BiLSTM
CNN-GRU
CNN-LSTM
CNN-RNN
Common Path Mining
Comparative Analysis : different methods
Convolutional networks
Convolutional Neural Network (CNN)
Convolutional neural networks
Convolutional Neural Networks (CNN)
Correlation feature selection
CW Attacks against GAN
DA
DATA mining Algorithms
DBN
DBSCAN
DCNNBiLSTM
Decision forest
decision jungle (DJ)
Decision Stump
Decision table
Decision Tree
Decision Tree (CART)
Decision Tree (DT)
Decision Tree (J48)
DECISION TREE ALGORITHM
Decision Tree as
decision tree based
decision tree.
Decision Trees
deep autoencoders
Deep Belief Network (DBN)
deep belief networks
deep Boltzmann machines
Deep CNN-BiLSTM Model
Deep Feed Forward (DFF)
deep feedforward neural network (DFNN)
Deep Learning (DL)
Deep networks
Deep Neural Network
Deep Neural Network (DNN)
Deep neural networks
Deep SARSA
Detection rate
DFF
DIGFuPAS
Discriminative Multinomial Naïve Bayes
DL
DL (Restricted Boltzmann Machine (RBM))
DML
DNN
DNN (Also Attack comparison of diff algo with the DNN proposed)
DNN Model.
DO-IDS
DP clustering
DRL training algorithm
DT
DT)
DTKNN
DTNB
e k-nearest neighbor (k-NN) algorithm
ELM
ELM based on modified K-means
EM
END (Ensembles of Balanced Nested Dichotomies for Multi-class Problems) . Grading
Enhanced Adaboost
Ensemble
Ensemble approach with WMV
Ensemble feature selection with information gain
ENSEMBLE LEARNING
Ensemble models
Extended RBFNN Within a Supervised Learning Framework
Extended RBFNN Within an Offline Reinforcement Learning Framework
Extra Tree
Extreme Gradient Boosting
Extreme Gradient Boosting (XGBoost)
Extreme Learning Machine (ELM)
False alarm
FC-ANN
Feature Selection (PCA)
federated deep learning
Federated learning
feed-forward
FFDNN
FFO-PNN
FGSM
FL-CNN
FP-ANK
Fuzzy
fuzzy clustering
fuzzy controller
Fuzzy Logic
Fuzzy Logics
Fuzzy rule-based modeling
GA
Gain Ratio Feature Evaluator) are applied to implement the NIDS models
GALR-DT
GAN
GAN-DNN
GASSIST-ADI
Gaussian Naive Bayes
GB
GBBK Algorithm
GBDT
GD-ANN
Generative models
Genetic Algorithm
Genetic Network Programming
GeneticSearch
GE-SVM
GNB
gradboost
Gradient Boost
Gradient Boosted Decision trees
Gradient Boosting
GRU
GRU-RNN
GS-ANN
GSPSO-ANN
GV-SVM
HC-DTTWSVM
HFS-LGBM
Hidden Naive Bayes
Hidden Naïve Bayes (HNB)
Hierarchical Clustering
HMM
Hoeffding
Hybrid SVM ELM
Hyperpipes
IBK
IBK algorithm
ICE
IDS
IDSGAN
IF
IG
IGR
IGRF-RFE
Inception
Inception transfer learning
Instance based learning
Isolation forest
Isolation Forest (iForest)
J.48
J4.8
J48
J-48
J48(C4.5)
JRIP
k Nearest Neighbor (KNN)
K NEAREST NEIGHBOR ALGORITHM
KA
Keras Deep Learning models
K-M
KMC
K-Mean
K-Means
K-Means Clustering
K-means Clustering algorithm
K-Medoids
KMSVM
KN neighbor
K-Nearest Neighbor
k-nearest neighbor (KNN)
k-nearest neighbor (k-NN) algorithm
k-nearest neighbors
K-Nearest Neighbors (KNN)
k-Nearest Neighbour (k-NN)
k-nearest neighbour classification
k-Nearest-Neighbors (KNN)
kNN
k-NN
KNN
K-NN
KNN Neighbors
KPCA-GA-SVM
KSVM
LCC-MI-SVM-FS
LDA
LGBM
Library for Support Vector Machine (LIBSVM)
Light Gradient Boosting Machine (LightGBM)
LightGBM
Linear
Linear Discriminant Analysis (LDA)
Linear Regression (LR)
linear support vector
LinearSVC
LMDRT-SVM
LMDRT-SVM2
LMT
Local Outlier Factor (LOF)
Logistic
Logistic Model Trees (LMT)
LOGISTIC REGRESSION
Logistic Regression (LR)
Long Short-Term Memory (LSTM)
LR
L-SCAN
LSTM
LuNET
LUS algorithm
LWL ALGORITHM
MABC-EPSO
machine leaning . CFS
Machine learning based ID3
MARK-ELM FPoly kernel set (proposed framework)
Markov chain
MCC
Meta (KNN) with Neighbors (K)
Meta (SMO)
MID-PCA combining PCA (Principal Component Analysis)
minimum redundancy maximum- relevance feature selection techniques
ML/DL/Methods Used (SVR
MLP
MLP.
Modified R
MOGF-IDS
MOPF
MPLCS
mRMR (minimum Redundancy Maximum Relevance – MID evaluation criteria)
Multi-
multiclass ensemble
Multilayer Perception
Multi-layer Perceptron
MULTILAYER PERCEPTRON
Multi-Layer Perceptron (MLP)
Multi-Layer Perceptron CS
Multilayer Perceptron neural network
Multi-Layer Perceptron(ANN)
multi-level hybrid SVM
multilevel semi-supervised ML (MSML) including K-means
Multinomial Naïve Bayes
muti classification)
N2B
NA
Naive Bayes
Naïve Bayes
Naive Bayes (NB)
Naïve Bayes (NB)
Naïve Bayes (NB) Algorithm
Naïve Bayes algorithm
Naïve Bayes Clustering
Naive Bayesian
Naïve Byes
NaiveBayes
Naïvebayes
NaiveBayes (NB)
Navie Bayes
NB
NB s are used
NB Tree
NBTree
Network Flow Traffic Feature Extraction
Neural Network
Neural network (ANN)
Neural Networks
N-KPCA-GA-SVM
NN
non-linear SVM
NPV
NSL-KDD
om Committee
om Forest
om Forest (RF)
OM FOREST ALGORITHM
om Forest Algorithm (T-SNERF)
om forest decision tree s
om Forest Importance
om forest s (MRFA)
om Forest.
om Forests
OM TREE
omForest
omizable
One class (OCC)
one-class SVM
OneR
Packet Network Traffic
PART R
PART. Several Tree-based
PCA
PCA-GA-SVM
PDP
PFI
PGD
presented causal model
Principal Component Analysis (PCA)
principal components analysis
Proposed
Proposed algorithm
proposed approach
Proposed deep SARSA
Proposed DL-RF
Proposed IDS
Proposed method (ABC-MLP)
Proposed model
Proposed Technique
PSO
PSO algorithm
PSO-ANN
PSO-RF
PSO-SVM
ptimised QNN
QCNN
QDA
QSVM
Quadratic discriminant Analysis
quantum convolutional neural network (QCNN)
quantum support vector machine (QSVM)
R
R/forest
R/tree
radial basis function
Radial Basis Function (RBF)
Rain Forest
RBF
RBF Network
RBF neural networks
RBM
Recurrent Neural Network (RNN)
Recurrent neural networks
Recursive Feature Elimination
Recursive-Feature-Elimination (RFE) features are selected
Regression
ReNN
REP Tree
REPTree
Resnet 152
Restricted Boltzmann Machine (RBM)
Restricted Boltzmann machines
RF
RF)
RFC
RFE-SMOTE
RFs
RIDOR
Ripple Rule
RMSE
RNN
RNN) etc. Write comma separated.
RNN-DAE
RNN-IDS
RNN-LSTM
Rough Set
Rough Set Theory (RST) based approach
RP
RTree
Rule-based ML algorithms are available in WEKA
S III
SA
SCDNN
Self taught learning (STL) : Sparse Autoencoder
Self-organizing Map
semi-supervised learning
SFFS
SGD
SGD s
SHAP
Simple Logistic
SimpleCart
Simulated annealing (SA)
single SVM
Single-SVM
SMO
SMO)
SMOTE
SMOTE to mitigate the class imbalance of the dataset
SOM
SO-SVM
Sparse auto-encoder
Stacked generalisation
Stacked LSTM
Stacking (Jrip
Stacking (KNN
Stacking Dilated Convolutional Autoencoders
STL
Stochastic Gradient Descent (SGD) combined with different feature selection techniques (Correlation Ranking Filter
Stochastic variant of Piramol estimated sub-gradient solver in SVM (SPegasos)
Supervised learning using LSTM
Support Vector Machine
Support Vector Machine (SVM)
support vector machine (SVM).
Support vector machine[
Support vector machines
Support Vector Machines (SVM)
Support Vector Machines (SVM).
Support Vector Machines (SVMs)
support-vector machine (SVM) with ant colony optimization (ACO)
Survey of different methods
Survey of different ML methods
SVC
SVM
SVM techniques
SVR used to create the model
TAN "Tree Augmented Naive Bayes
T-DFNN
T-Distributed Stochastic Neighbour Embedding
The CatSub
the Decision Tree based C4.5 (J48)
The paper gave investigation
the proposed HFS-LGBM
three-layer
tree based
TSE
Unsupervised Extreme Learning Machine (UELM)
Unsupervised learning using the deep autoencoder
VanilaRNN
VARIATIONAL AUTOENCODER
VGG 16
VGG16
WFEU
Xception
XGB
XGBoosT
XGBoost (with ANN
XGBoost to train an ML-based IDS
ZeroR
ZEROR ONER