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A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

https://doi.org/10.1002/dac.4401
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Pattern Recognition and Machine Learning
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Instance-Based Learning Algorithms
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Support-Vector Networks
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Modular neuron comprises of memristor-based synapse
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An effective combining classifier approach using tree algorithms for network intrusion detection
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Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data
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Neural networks for pattern recognition
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A novel hybrid KPCA and SVM with GA model for intrusion detection
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An overview of anomaly detection techniques: Existing solutions and latest technological trends
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A novel intrusion detection system based on hierarchical clustering and support vector machines
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A novel hybrid intrusion detection method integrating anomaly detection with misuse detection
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Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling
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A novel intrusion detection system by using intelligent data mining in weka environment
inferredcites →https://doi.org/10.1016/j.procs.2012.06.056openalex-snapshot
Tackling Application-layer DDoS Attacks
inferredcites →https://doi.org/10.1016/j.procs.2018.05.169openalex-snapshot
Network Intrusion Detection in Big Dataset Using Spark
inferredcites →https://doi.org/10.1016/j.proenv.2011.12.040openalex-snapshot
A method of SVM with Normalization in Intrusion Detection
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Stacked generalization
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2017 IEEE International Conference on Big Data and Smart Computing (BigComp)
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A detailed analysis of the KDD CUP 99 data set
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Network Anomaly Detection with Stochastically Improved Autoencoder Based Models
inferredcites →https://doi.org/10.1109/dasc-picom-datacom-cyberscitec.2016.32openalex-snapshot
Attack Detection in Cloud Infrastructures Using Artificial Neural Network with Genetic Feature Selection
inferredcites →https://doi.org/10.1109/etfa.2016.7733515openalex-snapshot
Accelerated deep neural networks for enhanced Intrusion Detection System
inferredcites →https://doi.org/10.1109/glocom.2009.5425454openalex-snapshot
SYN Flooding Attack Detection Based on Entropy Computing
inferredcites →https://doi.org/10.1109/icacci.2017.8126018openalex-snapshot
Evaluating effectiveness of shallow and deep networks to intrusion detection system
inferredcites →https://doi.org/10.1109/icccnt.2018.8494186openalex-snapshot
Network Intrusion Detection Using Clustering and Gradient Boosting
inferredcites →https://doi.org/10.1109/iccw.2018.8403759openalex-snapshot
Network Intrusion Detection Based on Stacked Sparse Autoencoder and Binary Tree Ensemble Method
inferredcites →https://doi.org/10.1109/icmla.2016.0040openalex-snapshot
Toward an Online Anomaly Intrusion Detection System Based on Deep Learning
inferredcites →https://doi.org/10.1109/icoac.2011.6165213openalex-snapshot
Optimized feature selection with k-means clustered triangle SVM for Intrusion Detection
inferredcites →https://doi.org/10.1109/icrito.2015.7359266openalex-snapshot
Design and development of a prototype application for intrusion detection using data mining
inferredcites →https://doi.org/10.1109/icsse32925.2014openalex-snapshot
2014 IEEE International Conference on System Science and Engineering (ICSSE)
inferredcites →https://doi.org/10.1109/ictke.2018.8612331openalex-snapshot
Anomaly-Based Network Intrusion Detection System through Feature Selection and Hybrid Machine Learning Technique
inferredcites →https://doi.org/10.1109/ijcnn.2017.7966342openalex-snapshot
Autoencoder-based feature learning for cyber security applications
inferredcites →https://doi.org/10.1109/iri.2018.00041openalex-snapshot
Tackling Class Imbalance in Cyber Security Datasets
inferredcites →https://doi.org/10.1109/istel.2016.7881798openalex-snapshot
A new feature selection IDS based on genetic algorithm and SVM
inferredcites →https://doi.org/10.1109/lsens.2018.2879990openalex-snapshot
Deep and Machine Learning Approaches for Anomaly-Based Intrusion Detection of Imbalanced Network Traffic
inferredcites →https://doi.org/10.1109/platcon.2016.7456805openalex-snapshot
Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection
inferredcites →https://doi.org/10.1109/rait.2012.6194493openalex-snapshot
A hybrid system for reducing the false alarm rate of anomaly intrusion detection system
inferredcites →https://doi.org/10.1109/siu.2017.7960180openalex-snapshot
Intrusion detection with autoencoder based deep learning machine
inferredcites →https://doi.org/10.1109/siu.2018.8404689openalex-snapshot
Computer network intrusion detection using sequential LSTM Neural Networks autoencoders
inferredcites →https://doi.org/10.1109/siu.2018.8404704openalex-snapshot
Computer network intrusion detection using various classifiers and ensemble learning
inferredcites →https://doi.org/10.1109/siu.2018.8404817openalex-snapshot
Network anomaly detection with deep learning
inferredcites →https://doi.org/10.1109/surv.2010.032210.00054openalex-snapshot
An Overview of IP Flow-Based Intrusion Detection
inferredcites →https://doi.org/10.1109/tetci.2017.2772792openalex-snapshot
A Deep Learning Approach to Network Intrusion Detection
inferredcites →https://doi.org/10.1109/wincom.2016.7777224openalex-snapshot
Deep learning approach for Network Intrusion Detection in Software Defined Networking
inferredcites →https://doi.org/10.1145/2513228.2513281openalex-snapshot
Combining principal component analysis, decision tree and naïve Bayesian algorithm for adaptive intrusion detection
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-snapshot
XGBoost
inferredcites →https://doi.org/10.1145/997150.997156openalex-snapshot
A taxonomy of DDoS attack and DDoS defense mechanisms
inferredcites →https://doi.org/10.14569/ijacsa.2016.070159openalex-snapshot
Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
inferredcites →https://doi.org/10.17706/ijcce.2018.7.2.20-31openalex-snapshot
Intelligent Anomaly Detection Techniques for Denial of Service Attacks
inferredcites →https://doi.org/10.2307/2347628openalex-snapshot
Ridge Estimators in Logistic Regression
inferredcites →https://doi.org/10.3390/electronics8020182openalex-snapshot
On Energy Efficiency and Performance Evaluation of Single Board Computer Based Clusters: A Hadoop Case Study
inferredcites →https://doi.org/10.3390/electronics8030322openalex-snapshot
Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection
inferredcites →https://doi.org/10.3390/info9010001openalex-snapshot
EmoSpell, a Morphological and Emotional Word Analyzer
inferredcites →https://doi.org/10.3390/info9070149openalex-snapshot
Effective Intrusion Detection System Using XGBoost
inferredcites →https://doi.org/10.4108/eai.3-12-2015.2262516openalex-snapshot
A Deep Learning Approach for Network Intrusion Detection System
inferredcites →https://doi.org/10.4236/jis.2018.94016openalex-snapshot
Generation of DDoS Attack Dataset for Effective IDS Development and Evaluation
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.48550/arxiv.1810.11363openalex-snapshot
CatBoost: gradient boosting with categorical features support

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Classification of Multiclass DDOS Attack Detection Using Bayesian Weighted Random Forest Optimized With Gazelle Optimization Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-031-25538-0_28openalex-snapshot
Hiatus: Unsupervised Generative Approach for Detection of DoS and DDoS Attacks
inferred← citeshttps://doi.org/10.1007/s00500-023-08960-wopenalex-snapshot
RETRACTED ARTICLE: Transforming sports training through the integration of internet technology and artificial intelligence
inferred← citeshttps://doi.org/10.1007/s13369-022-07517-7openalex-snapshot
Use of Machine Learning for Web Denial-of-Service Attacks: A Multivocal Literature Review
inferred← citeshttps://doi.org/10.1007/s41870-022-01003-xopenalex-snapshot
DDoS attack detection using MLP and Random Forest Algorithms
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GraphUnet-SS: A novel deep learning model for protein secondary structure prediction based on U-Net architecture
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Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation
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An efficient network intrusion detection approach based on logistic regression model and parallel artificial bee colony algorithm
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A dynamic multiple classifier system using graph neural network for high dimensional overlapped data
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A robust and statistical analyzed predictive model for drug toxicity using machine learning
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Machine learning method in detecting a distributed of service (DDoS): A systematic literature review
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Performance analysis of ensemble learning algorithms in intrusion detection systems: A survey
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A literature review on machine learning for computer network
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An Algorithm for Detection of Traffic Attribute Exceptions Based on Cluster Algorithm in Industrial Internet of Things
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XMODOS: An Explainable Model for Denial of Service Attack Detection
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A Deep 1-D CNN and Bidirectional LSTM Ensemble Model With Arbitration Mechanism for LDDoS Attack Detection
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MLPNN and Ensemble Learning Algorithm for Transmission Line Fault Classification
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Detecting Distributed Denial of Service in Network Traffic with Deep Learning
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Performance Evaluation of Machine Learning Classifiers for Predicting Denial-of-Service Attack in Internet of Things
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Developing Novel Deep Learning Models to Detect Insider Threats and Comparing the Models from Different Perspectives
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Evrişimsel Sinir Ağları Tabanlı Derin Öğrenme Yöntemiyle Müşteri Şikayetlerinin Sınıflandırılması
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Entropy-Guided Jaya optimization for robust feature selection in high-dimensional network attack detection
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Pendekatan Machine Learning Dengan Menggunakan Algoritma Xgboost (Extreme Gradient Boosting) Untuk Peningkatan Kinerja Klasifikasi Serangan Syn
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Machine Learning for Detection of Distributed Denial-of-Service Attacks from Queries Executed in DBMS
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A Comprehensive Review of Deep Learning Techniques for the Detection of (Distributed) Denial of Service Attacks
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