ml algo

Identify ml classfication algos from this list:

(ACO)
(CNN-LSTM)
(DIC) algorithm
1-D CNN
1D Convolutional Neural Network (1dCNN)
1D-CNN
2D-CNN
A Lite BERT (ALBERT)
AB
ABC
AC
Ada Boosting
ADABoost
AdaBoost classifier
AdaBoost-A
Adam optimizer
Adaptive Alarming Module (AAM)
Adaptive Thresholding with Early Stopping (ATES)
Adaptive Variational Autoencoder
Adaptively federated multi-task learning
ADH
Advanced Encryption Standard AES
Adversarial Training (AT)
AE
Algorithmic-based detection systems.
ALWM-ResNet
AutoEncoder
dynamic
k-means clustering
Poisson regression
Random Forest (RF)
Sustainable Ensemble Learning method
SVM
ANN
ANN-MLP
Anomaly-based Detection
Ant colony optimization
Ant Lion Optimization (ALO)
AODV (Ad-Hoc on Dem
Vector)
ARM
Artificial bee colony
Artificial Neural Networks
Artificial Neural Networks (ANN)
Artificial neural networks (ANNs)
Artificial Neural Networks.
Artificial Optimization (AO)
ASC.
Asynchronous FL-CNN-GRULSTM
AttackNet
Attention
Attention-
Attention Mechanism
Attention-based LSTM
AUC
Augmentation
Auto Encoder (AE)
AutoEncoder
Auto-Encoder
Autoencoder-based IDS
Autoencoders
AutoGluon
AutoML
Bagging
bagging ensemble
Bagging: Random Forest
Basic Autoencoder (AE)
Batch reinforcement learning
Bayes Classifiers
Bayesian Autoencoder (BAE)
Bayesian Logic Regression (BLR)
Bayesian Network Models
Bayesian Neural Networks (BNN)
Bayes-Net
BayesNet Algorithm
BBO algorithm
Benign
Bidirectional Encoder Representations from Transformers (BERT)
Bidirectional Long Short-Term Memory (BiLSTM)
Big-Bang Big-Crunch (BBBC) optimization
BiGRU
BiLSTM
Bi-LSTM
Bi-LSTM
Bi-GRU
Blockchain
BN
BN firefly
BoAu
BODL-ADC
Boltzmann Machine (BM)
Boosting
Boosting (XG-Boost)
Boosting: ADABoost
BoSC-10
BoSC-3
BoSC-4
Bot
botnet
BPEO algorithm
Broker Algorithm
C&W technique
C5
CA
CAFECNN
CANET
Carlini & Wagner (C&W)
CART
CART (Classification
Regression Trees)
CART Decision Tree (DT)
Cascade Forward Back Propagation Neural Network (CFBPNN)
CBRNN
CE
Centralized Blending
Chebyshev Distance
Chi-square
Classification
cloud–fog
Clustering
CNN
CNN + LSTM
CNN neural network
CNN-based IDS
CNN-BiLSTM
CNN-CapSA
CNN-DSFO
CNN-GRU
CNN-LSTM
CNN-MLP
CNNs
CNN-SAE
COF
Composite Recurrent Neural Networks (CRNN)
computing
Conditional Generative Adversarial Network – Gradient Penalty (WCGAN-GP)
Genetic Algorithm (GA).
Conditional Variational Autoencoder (CVAE)
Convolutional Neural Network
Convolutional Neural Network (CNN)
Convolutional Neural Networks (CNN)
COREBOT
CORES
Correlation analysis (CA)
Cost-sensitive deep learning
Cramer’s V
CTGAN.
Cuda-Bidirectional Long Short-Term Memory (BLSTM)
Cuda-Deep Neural Network
Custom KNN
DA
DAE
DANN
Data augmentation
Davies-Meyer Compression Function
DBM
DBN
DBN classifier
DBN-LSTM
DBNs
DBSCAN.
DCCNN-SMO
DCNN
DCNNBiLSTM
DCRNN (Deep Residual Convolutional neural network)
DDoS
DDQN
DE
Decentralized learning.
Decision Tree
Decision Tree (DT)
Decision Tree
Random forest
Decision Trees
Decision Trees (DT)
Deep Belief Network (DBN)
Deep Belief Networks
Deep Belief Networks (DBN)
Deep Contractive Autoencoder (DCAE)
Deep Learning
Deep Learning (DL)
Deep Neural Network
Deep Neural Network (DNN)
Deep Neural Networks (DNN)
Deep Q-Learning
Deep Q-Learning (DQL)
Deep reinforcement learning
Deep Sparse AutoEncoder
DeepAK-IoT
DeepLog
Default RBF-SVM
Deliberate Deep Reinforced Learning (DDRL) classification
Denoising Autoencoder
Deep Feedforward Neural Network
Descriptive Statistical Methods
DFF
DGA
Dice Loss
Differential evolution
Differential Private Stochastic Gradient Descent (DP-SGD)
Digital Signature
Digital Twin
Discriminative Principal Component Analysis (DPCA)
Distributed Stochastic Gradient Descent (SGD)
DL
DL-based Deep Packet inspection
DNN
DNN-based IDS
DNN-RNN
DoS GoldenEye
DoS Hulk
DoS Slowhttptest
DoS slowloris
DPA-FL (Defense against Poisoning Attacks in Federated Learning)
DPMM
DQL
DQN
DQN(Deep Q-Networks) - HIDS
DRL
DT
DT (Decision Tree)
DT (Ens-DT)
DTC
DT-ILIS
DTL
dynamic itemset counting
Dynamic Segmentation.
e KNearest Neighbors (KNN)
EECA-LSTM
Efficient-Net
E-GraphSAGE
EL
ELIDS
ELM
Elman recurrent neural network
EMLNN
EMRFT
Encoder–decoder neural network.
Ensemble Learning
Ensemble method selection
Ensemble Modeling
ET
ETC
E-Trees.
Euclidean Distance
Evaluation metrics
Extra Tree (ET)
Extra-Tree
ExtraTrees
eXtreme Gradient
Extreme Gradient Boosting
Extreme Gradient Boosting (XGBoost)
Fast Gradient Sign Method (FGSM)
FastABOD
FastAI
FCM+ SVM
FDL-IDM (Federated Learning-based Intrusion Detection Model)
FedAvg
Federated Learning
Federated Learning (FL)
FedProx
Feed Forward.
Feed-Forward Neural Network
Ferrag et al.
FFA
FFDNN.
FFNN
FGSM
Firefly optimization algorithm
FL
Flow Expiration Manager
FNN
Focal Loss
Friha et al
FS-DL
FTP-Patator
Fully Convolutional Neural Network
Fully-connected NN
Fusion Multi-Tier DNN
Fuzzy C-means
Fuzzy C-means
Bi-LSTM Algorithms
fuzzy logic
GA
GA-LR
game theory algorithm (GTA)
GAN
GANs
Gated Recurrent Unit (GRU)
Gated Recurrent Unit.
Gated Recurrent Units (GRU)
Gaussian Data Augmentation (GDA)
GB
GBDT
GBM
GBM (Gradient Boosting Machine)
GBT
GCNN
GCNN (Graph Convolutional Neural Network)
GDA Gaussian Data
Generative Adversarial Network (GAN)
Generative Adversarial Networks (GANs)
Generative Pre-trained Transformer 2
Genetic Algorithm
Genetic Algorithm (GA)
genetic algorithms
Genetic Algorithms (GA)
GHLBO-based DSA
GLC
G-Means
GNB
GNN
GOA
Gorilla Troops Optimization (GTO) algorithm
GPCNN
Gradient
Gradient Boosting
Gradient Boosting Decision Tree (GBDT)
Gradient XGBoost
Graph Convolutional Neural Network (GCNN)
Graph Neural Network (GNN)
Graph Referencing Normalization (GRN) algorithm
Grasshopper optimization algorithm
Gravitational Search Algorithm.
Gray wolf optimization algorithm
Grey Wolf Optimization (GWO)
GRU
GRU.
GWO
hard voting ensemble.
HBO
HBOS
HCRNNIDS
Heartbleed
HFL-HLSTM
HGBC
HHH
High Confidence (HC)
HiLSTM
HMM
HSR-WSNs
Hybrid AdaBoost-Random Forest
Hybrid Classifier
Hybrid Model
Hybrid Models
Hybrid packet inspection
Hyperparameter tuning
IBK
IBL
ICDBN-IDM
IDS
IDS-DBN
IDS-DCNN
IFA.
iForest
IGOA (Improved Gazelle Optimization Algorithm)
IG-PCA
Image Classification
Inception-Net
InceptionV3
including Logistic Regression (LR)
Infiltration
Input Vector Matrix (IVM)
Internet of things
IoMT
Isolation Forest
Isolation Forest (IForest) Algorithm
Isolation Forest (IsolationForest) [26]
Isolation-Forest
ITCM-KNN
Iterative Dichotomiser 3 (ID3)
Iterative Model Averaging
Iterative Pruning Network
J48
Jacobian Saliency Map Attack (JSMA)
K means
K Nearest Neighbor
KerasClassifier
kernel ELM
kernel PCA
Kernel-Support Vector Machines
K-FoldCrossValidation
KL Divergence
KMC
K-Means
K-Nearest Neighbor
K-Nearest Neighbors
k-Nearest Neighbors (kNN)
k-Nearest Neighbors (k-NN)
K-Nearest Neighbors (KNN)
K-nearest Neighbors.
kNN
k-NN
KNN
KNN-PCA
K-SVM
Kuhn-
LabelEncoder
the SimpleImputer
Large Language Model Prompt Engineering
LASSO regression
LB
LDA
LDCOF
Learning-Driven Detection Mechanism (LEDEM)
Least-Square SVM
LGB
LightGBM
LIME (Local Interpretable Model-agnostic Explanations)
linear
Linear SVM
Linear Weighted Projection Regression
LLM
LMNL
LOF
Logistic Regression
Logistic Regression (LR)
Logistic Regression Model
Long Short-Term Memory (LSTM)
Long Short-Term Memory (LSTM) networks
Long Short-term Memory Networks
LP
LR
LR (Logistic Regression)
LSTM
LSTM Autoencoder
LSTM/CNN
LSTM+CNN
LSTM+Spark
LSTM-based IDS
LSTM-KPCA
LSTM-RNN
LSVM
LWA (Learning With an Adversary)
Machine Learning
MaMPF
MAMRL
Manhattan Distance.
Markle Tree: A tree structure used for data authentication MT
Markov Decision Process
Markov Decision Process (MDP)
Masking
MCA-LSTM
M-CNN
MD block
Message Queuing Telemetry Transport MQTT
MFS-MCDM
MI
ML with PCC
IF
MLP
MLP + Fuzzy
MLP 16
MLP. RNN
MMP
MobileNetV3
Modified Archery Algorithm
MOE
MOIA
MRD module
MRMR
MRN
Multilayer Perceptron
multi-layer perceptron (MLP)
Multilayer Perceptron (MLP)
Multi-Layer Perceptron (MLP)
multi-layer Transformer encoder
Multinomial Naive Bayes (MNB)
multi-party data model
Multi-Scale Convolutional Neural Network
Munkres algorithm
Mutual information (MI)
Naive Bayes
Naïve Bayes
Naive Bayes (NB)
Naïve Bayes classifiers
NaiveBayes
Natural Language Processing
NAV
NB
NBGOA (Novel Binary Grasshopper Optimization Algorithm)
NDAE
Network
Neural
Neural Network
Neural-network
NIDS
NIDS classifiers
NN
NN
Genetic algorithm
NNs
non-LP (NLP)
OCSA
OCSVM
ODIN
OLS
One-Class Support Vector Machine (OC-SVM)
One-Class Support Vector Machines(oSVM) [27]
One-Class SVM
On-ILIS
optimization (PSO)
Ordered Probit regression
OSVM
PART
PART algorithm
Particle Bee Colony Swarm (PBCS)
Particle Swarm Optimization
Particle Swarm Optimization (PSO)
PCA
PCC
PC-IDS
PCNN
PENCIL
PG
PGD-AML
Policy Engine
Policy Administrator Module
Polynomial-SVM
PortScan
Post-Processing Flows
Principal Component Analysis (PCA)
programming
programming (LP)
Projected Gradient Descent (PGD)
Proposed DBN
Proposed GRU-CNN
Proposed HFS-ERF
Proposed MM-WMVEDL
PS-IPS
PSO
PSO-PNN
Publisher side algorithm
py-Custom
PYKSPA
Q-Learning
QML
Quadratic Discriminant Analysis (QDA)
RAkEL
RAMDO
RAMNIT
RandNN
Random Forest
Random Forest (RF)
Random Forests (RFs)
Random Projection
Random Sampling
Random Space (RS)
Random Subspace Learning
Random Subspace Learning
K-Nearest Neighbor (RSL-KNN).
Random Tree (RT)
Random-Forest
Randomized Matrix Factorization.
RBM
Recurrent Neural Network
Recurrent Neural Networks (RNN)
Recursive feature elimination
ReduceLROnPlateau
refined Bayesian equilibrium
Reinforcement Learning
Reinforcement learning (RL)
ReLU
ReLU activation function.
ResNet50
ResNet50–1D-CNN
RESNETCNN
ResNet-GRU
ResNeXt
RF
RFA
RFAA
RFC
RF-SVM
RL
RNN
RNN) etc. Write comma separated.
RNN.
ROS (Random Over Sampling)
RPFMI
RT
Rule-Based Algorithm
Rule-Based Classifiers
RUS (Random Under Sampling)
SAE
SDA
SDN-based IPS
SDO
Self-Organizing Maps (SOM)
Self-supervised Learning Mechanism
Sequence data generation algorithm
SGD
SHAP (Shapley Additive exPlanations)
Shapley Additive explanation (SHAP) analysis
Partial Dependency Analysis (PDA)
Siamese Neural Network
Sigmoid
Sigmoid-SVM
Signature-based Detection.
SIMDA
Simple Multi-Tier DNN
Simple RNN
Simple Stacking
Single DNN
SLaveMPF
SMF
SMOTE
SMOTE (Synthetic Minority Over-sampling Technique)
S-NDAE
soft voting
SOM
SPN
SqueezeNet
SRC
SSAE
SSH-Patator
Stack Ensemble Learning
stacked autoencoder (SAE)
Stacked Auto-Encoders (SAE)
Stacked Ensemble.
Stacked sparse VAE
Stacked unsupervised FL
Stacked+ Autoencoder+SVM
StandardScaler
STIDE-3
STIDE-4
Stochastic Gradient Descent (SGD)
Stochastic Gradient Descent one-Class Support Vector Machines (SGD-oSVM) [28]
Streebog Cryptographic Substitution-Permutation Network (SCSPN)
Subscriber Algorithm
supervised classifier
Supervised Learning
Support Vector Machine
Support Vector Machine (SVM)
Support Vector Machines (SVMs)
Support Vector Regression (SVR)
SVD (Singular Value Decomposition)
SVM
SVM (Support Vector Machine)
SVM-NN
SVMs
SVR
TabNET
TCN
Temporal Convolutional Network (TCN)
TF-IDF
The paper compares the performance of the GAN-LSTM method against several other classification models
The particle swarm
Three-layer neural network
Traditional LSTM
Traditional Seq2Seq Encoder–Decoder
Traffic-aware Self-supervised Learning for IoT Network Intrusion Detection System (TS-IDS)
train decision tree (DT)
Training-Free Clustering Algorithm
Transfer Fuzzy Learning (TFL)
Transfer Learning
Transfer Learning Methodology
Transformer
Tri-layered NN
TSCRNN
UDAE algorithm
UNet++
VAERNN-AD
Variational Autoencoder (VAE)
Variational Bayes
Variational Inference: Used in CNN-LSTM
GRU-based BDL for time-series TCP/IP packets anomaly detection
VGG-16
VGG19
VGG-19
Voting Ensemble
Waikato Environment for Knowledge Analysis (WEKA)
Wasserstein
Web Attack: Brute Force
Web Attack: Sql Injection
Web Attack: XSS
Weighted Average Probabilities.
WFEU FFDNN
WOA
XGB
XGBoost
XG-Boost
XGBoost.
Z-Score normalization