Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)

Journals Menu

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Intelligent Learning Systems and Applications

ISSN Print: 2150-8402
ISSN Online: 2150-8410
www.scirp.org/journal/jilsa
E-mail: jilsa@scirp.org

Google-based Impact Factor: 1.5
Citations h5-index & Ranking*

Journals Menu

"Support Vector Machine and Random Forest Modeling for Intrusion Detection System (IDS)"
written by Md. Al Mehedi Hasan, Mohammed Nasser, Biprodip Pal, Shamim Ahmad,
published by Journal of Intelligent Learning Systems and Applications, Vol.6 No.1, 2014

has been cited by the following article(s):

Google Scholar
CrossRef

[1]	Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations
	International Journal of …, 2022

[2]	Analyzing sensitivity of flood susceptible model in a flood plain river basin
	Geocarto International, 2022

[3]	Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling
	Remote Sensing, 2022

[4]	A hybrid machine learning model for intrusion detection in VANET
	Computing, 2022

[5]	Intrusion detection of industrial internet-of-things based on reconstructed graph neural networks
	IEEE Transactions on …, 2022

[6]	Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models
	Scientific Reports, 2022

[7]	RANet: Network intrusion detection with group-gating convolutional neural network
	Journal of Network and …, 2022

[8]	Contextual anomaly detection on time series: a case study of metro ridership analysis
	Neural Computing and …, 2022

[9]	Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine
	Applied Sciences, 2022

[10]	Intelligent One-Class Classifiers for the Development of an Intrusion Detection System: The MQTT Case Study
	Mata, H Alaiz-Moretón… - Electronics, 2022

[11]	SCADA Vulnerabilities and Attacks: A Review of the State-of-the-Art and Open Issues
	Computers & Security, 2022

[12]	An Abnormal Traffic Detection Model Combined BiIndRNN With Global Attention
	IEEE Access, 2022

[13]	Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional …
	Applied Intelligence, 2022

[14]	An Explainable Deep Neural Framework for Trustworthy Network Intrusion Detection
	2022 10th IEEE International …, 2022

[15]	Network traffic verification based on a public dataset for IDS systems and machine learning classification algorithms
	2022 45th Jubilee International …, 2022

[16]	Evaluating the Performance of Various SVM Kernel Functions Based on Basic Features Extracted from KDDCUP'99 Dataset by Random Forest Method for Detecting …
	Wireless Personal …, 2022

[17]	Ensemble learning-based IDS for sensors telemetry data in IoT networks
	Mathematical …, 2022

[18]	Shaping future low-carbon energy and transportation systems: Digital technologies and applications
	iEnergy, 2022

[19]	Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods
	Applied Sciences, 2022

[20]	Machine learning combating DOS and DDOS attacks
	International Journal of …, 2022

[21]	Depression Anxiety Stress Scale and Handgrip using Machine Learning Analysis
	2022 4th International …, 2022

[22]	Network intrusion detection using Machine Learning approach
	Proceedings of the 12th …, 2022

[23]	IEEESEM
	2022

[24]	Developing machine learning methods for classification and analysis of dichotic listening using neurophysiological data
	2022

[25]	An Innovative Network Security Regulations Dependent on Improved Support Vector Machine from the Outlook of Modern Cities
	Scientific Hub of Applied Research in …, 2022

[26]	Transferability of Intrusion Detection Systems Using Machine Learning between Networks
	2021

[27]	A Machine Learning Approach for Uniform Intrusion Detection
	2021

[28]	Identificação das Dunas do Atacama (Norte do Chile) a partir da avaliação de três algoritmos no Google Earth Engine
	Revista Brasileira de …, 2021

[29]	Intrusion Detection System for MQTT Protocol Based on Intelligent One-Class Classifiers
	Mata, H Alaiz-Moretón… - Sustainable Smart Cities …, 2021

[30]	Machine Learning for Threat Recognition in Critical Cyber-Physical Systems
	2021 IEEE International …, 2021

[31]	A Tree Based Machine Learning and Deep Learning Classification for Network Intrusion Detection
	Avrupa Bilim ve Teknoloji Dergisi, 2021

[32]	Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection
	2021 International Conference …, 2021

[33]	A Powerful Ensemble Learning Approach for Improving Network Intrusion Detection System (NIDS)
	2021 Fifth International …, 2021

[34]	IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection
	Electronics, 2021

[35]	PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks
	arXiv preprint arXiv …, 2021

[36]	A Review on Intrusion Detection System Using a Machine Learning Algorithms
	International Conference on Emerging …, 2021

[37]	Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning
	Sensors, 2021

[38]	Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis
	Engineering Geology, 2021

[39]	ACGANs-CNN: A Novel Intrusion Detection Method
	2021

[40]	A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods
	2021

[41]	Forecast and anomaly detection on time series with dynamic context. Application to the mining of transit ridership data.
	2021

[42]	DS-kNN: An Intrusion Detection System Based on a Distance Sum-Based K-Nearest Neighbors
	2021

[43]	Resource Efficient Boosting Method for IoT Security Monitoring
	2021

[44]	Intelligent Machine Learning Approach for CIDS—Cloud Intrusion Detection System
	2021

[45]	Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms
	2021

[46]	Retracted: DETECTING ATTACKS ON MQTT-IOT PROTOCOL USING ML TECHNIQUES
	2020

[47]	Reliable Forest Fire Detection System Using Wireless Sensor Networks and Internet of Things
	2020

[48]	Using Parametric t-Distributed Stochastic Neighbor Embedding Combined with Hierarchical Neural Network for Network Intrusion Detection.
	2020

[49]	A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms
	2020

[50]	Intrusion detection of imbalanced network traffic based on machine learning and deep learning
	2020

[51]	Attackers are not stealthy: Statistical analysis of the well-known and infamous KDD network security dataset
	2020

[52]	Assessment of Machine Learning Techniques for Building an Efficient IDS
	2020

[53]	A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine
	2020

[54]	A Semi-supervised Intrusion Detection Algorithm Based on Auto-encoder
	2020

[55]	A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms.
	Webology, 2020

[56]	Advances in Modelling and Analysis B
	Journal homepage: http://iieta …, 2020

[57]	Predictor selection and attack classification using random forest for intrusion detection
	2020

[58]	A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms
	2020

[59]	Computational method to prove efficacy of datasets
	2020

[60]	IGAN-IDS: An Imbalanced Generative Adversarial Network towards Intrusion Detection System in Ad-hoc Networks
	2020

[61]	Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction
	2020

[62]	Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
	2020

[63]	Machine Learning Method for Cyber Security Intrusion Detection for Industrial Control Systems (ICSS)
	2020

[64]	Tuning to Optimize SVM Approach for Breast Cancer Diagnosis with Blood Analysis Data
	2020

[65]	Dimensionality Reduction for DDOS Database Using PCA
	2020

[66]	Systematic Literature Survey on IDS Based on Data Mining
	2019

[67]	Improving Situation Awareness Through Monitoring Data Correlation
	2019

[68]	SIGMA: Strengthening IDS with GAN and Metaheuristics Attacks
	2019

[69]	A Machine Learning Approach for Network Traffic Analysis using Random Forest Regression
	ACET Journal of Computer Education & Research, 2019

[70]	Fuzzy Automaton-based Early Detection Model
	2019

[71]	An Intrusion Detection Model based on a Convolutional Neural Network
	2019

[72]	Exploration of Cervical Myelopathy Location From Somatosensory Evoked Potentials Using Random Forests Classification
	2019

[73]	Intensive Pre-Processing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
	2019

[74]	Ensemble-based semi-supervised learning approach for a distributed intrusion detection system
	2019

[75]	Rade: Resource-efficient supervised anomaly detection using decision tree-based ensemble methods
	2019

[76]	Hybrid Architecture for Distributed Intrusion Detection System.
	2019

[77]	Multiclass classification procedure for detecting attacks on MQTT-IoT protocol
	2019

[78]	A Novel Intrusion Detector Based on Deep Learning Hybrid Methods
	2019

[79]	Analysis of NSL KDD Dataset Using Classification Algorithms for Intrusion Detection System
	2019

[80]	A Data-Driven Network Intrusion Detection Model Based on Host Clustering and Integrated Learning: A Case Study on Botnet Detection
	2019

[81]	Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc Networks
	2019

[82]	Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study
	2019

[83]	Hybrid Architecture for Distributed Intrusion Detection System
	Ingénierie des Systèmes d’Information (ISI), 2019

[84]	ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features
	2019

[85]	Apache spark ve makine öğrenmesi algoritmaları ile ağ saldırısı tespiti
	2019

[86]	Classifying Building Usages: A Machine Learning Approach on Building Extractions
	2018

[87]	Coupled Kernel Ensemble Regression
	International Journal of Computer Applications, 2018

[88]	Securing Big Data Ecosystem with NSGA-II and Gradient Boosted Trees Based NIDS Using Spark
	2018

[89]	Comparison of Performance Between Incremental and Batch Learning Method for Information Analysis of Cyber Surveillance and Reconnaissance
	2018

[90]	DoS Attack Detection Using Machine Learning and Neural Network
	2018

[91]	Co-regularized kernel ensemble regression
	World Wide Web, 2018

[92]	Semi-Supervised Machine Learning for Network Intrusion Detection
	ProQuest Dissertations Publishing, 2018

[93]	Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems
	2018

[94]	NIDS: Neural Network based Intrusion Detection System
	2018

[95]	An Abnormal Behavior Detection Based on Deep Learning
	2018

[96]	Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
	2018

[97]	Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
	2018

[98]	An Effective Way of Cloud Intrusion Detection System Using Decision tree, Support Vector Machine and Naïve Bayes Algorithm
	2018

[99]	Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning
	2018

[100]	Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
	2018

[101]	WebAD: A Cascading Model Based on Machine Learning for Web Attacks Detection
	Security and Privacy in Communication Networks, 2018

[102]	A novel method combining fuzzy SVM and sampling for imbalanced classification
	International Journal of Applied Systemic Studies, 2018

[103]	LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network
	Security and Communication Networks, 2018

[104]	Network Intrusion Detection on Apache Spark with Machine Learning Algorithms
	Engineering Applications of Neural Networks, 2018

[105]	Significant Metabolites and Outlier-Robust Classifier Identification for Breast Cancer Prediction
	Current Metabolomics, 2018

[106]	iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines
	2017

[107]	Prediction of protein subcellular localization using support vector machine with the choice of proper kernel
	2017

[108]	Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques
	2017

[109]	Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques تاينقت مادختساب تايفشتسملا يف ةعشلأا ءابطأ ةيجاتنإ نيسحت بيقنت …‎
	2017

[110]	Protein subcellular localization prediction using multiple kernel learning based support vector machine
	Molecular BioSystems, 2017

[111]	iMulti-HumPhos: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins Using Multiple Kernel Learning Based Support Vector Machine
	Molecular BioSystems, 2017

[112]	A Novel Unsupervised Anomaly Detection Approach for Intrusion Detection System
	2017

[113]	HAST-IDS: Learning Hierarchical Spatial-Temporal Features using Deep Neural Networks to Improve Intrusion Detection
	2017

[114]	Semi-supervised Random Forest for Intrusion Detection Network
	MAICS, 2017

[115]	A novel network security algorithm based on improved support vector machine from smart city perspective
	Computers & Electrical Engineering, 2017

[116]	Multi-Task Learning for Intrusion Detection on web logs
	Journal of Systems Architecture, 2017

[117]	A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
	CATENA, 2017

[118]	predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue
	Analytical Biochemistry, 2017

[119]	Cyber-physical intrusion detection for robotic vehicles
	2017

[120]	Study on credit evaluation of electricity users based on random forest
	2017

[121]	An approach to develop a hybrid algorithm based on support vector machine and Naive Bayes for anomaly detection
	2017 International Conference on …, 2017

[122]	多断面相关性区间预测法在短期交通流预测中的应用
	电子设计工程, 2017

[123]	Multiple Kernel Learning And Its Application In Bioinformatics
	2017

[124]	Identifying Intrusions in Computer Networks using Principal Component Analysis and Random Forest
	International Journal of Computer Science and Information Security, 2016

[125]	Semi-Supervised Deep Neural Network for Network Intrusion Detection
	2016

[126]	Relationship between Effective Application of Machine Learning and Malware Detection: A Quantitative Study
	ProQuest Dissertations Publishing, 2016

[127]	Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
	2016

[128]	Towards a multi‐layers anomaly detection framework for analyzing network traffic
	Concurrency and computation: practice and experience, 2016

[129]	Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems
	2016

[130]	A Granular Classifier By Means of Context-based Similarity Clustering
	J Electr Eng Technol., 2016

[131]	Anomaly Detection with ANN and SVM for Telemedicine Networks
	International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2016

[132]	A decision tree-based rule formation with combined PSO-GA algorithm for intrusion detection system
	International Journal of Internet Technology and Secured Transactions, 2016

[133]	Random forest modeling for network intrusion detection system
	Procedia Computer Science, 2016

[134]	Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems.
	2016

[135]	Applying Big Data Analytics Into Network Security: Challenges, Techniques and Outlooks
	2016

[136]	A Novel Anomaly Detection Approach for Mitigating Web-Based Attacks Against Clouds
	2015

[137]	An anamoly based Intrusion Detection System for mobile ad-hoc networks using genetic algorithm based support vector machine
	Advances in Natural and Applied Sciences, 2015

[138]	Hybrid Modified-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
	The Scientific World Journal, 2015

[139]	An information hiding system based on high frame rate video
	2015

[140]	Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection
	ProQuest Dissertations Publishing, 2015

[141]	Hybrid Modified 𝐾-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
	2015

[142]	Research Article Hybrid Modified 𝐾-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
	Yaseen, ZA Othman, 2015

[143]	An Integrated Approach for Intrusion Detection using Computational Methods
	Indian Journal of Science and Technology, 2015

[144]	An evolutionary fuzzy genetic and Naïve Bayesian approach for multivariate data classification
	Computer and Information Technology (ICCIT), 2014 17th International Conference on, 2014

[145]	An Evolutionary Fuzzy Genetic and Na?ve Bayesian Approach for Multivariate Data Classification
	2014

[146]	MARK-ELM: Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection
	Expert Systems with Applications, 2014

[147]	Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal


[148]	Hybrid Architecture for Distributed Intrusion Detection System Using Semi-supervised Classifiers in Ensemble Approach Hybrid Architecture for Distributed …
	SR Khonde

[149]	Canet: A Hierarchical Cnn-Attention Model for Network Intrusion Detection


[150]	NETWORK TRAFFIC DETECTION THROUGH MACHINE LEARNING


[151]	Anomaly Detection in Netflow Network Traffic Using Supervised Machine Learning Algorithms

[1]	Veri Ölçekleme ve Eksik Veri Tamamlama Yöntemlerinin Makine Öğrenmesi Yöntemlerinin Başarısına Etkisinin İncelenmesi Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2023 DOI:10.29130/dubited.948564

[2]	Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning Scientific Reports, 2023 DOI:10.1038/s41598-023-30851-1

[3]	Optimal Channels and Features Selection Based ADHD Detection From EEG Signal Using Statistical and Machine Learning Techniques IEEE Access, 2023 DOI:10.1109/ACCESS.2023.3264266

[4]	Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks Applied Intelligence, 2023 DOI:10.1007/s10489-022-03995-2

[5]	Veri Ölçekleme ve Eksik Veri Tamamlama Yöntemlerinin Makine Öğrenmesi Yöntemlerinin Başarısına Etkisinin İncelenmesi Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 2023 DOI:10.29130/dubited.948564

[6]	An Advanced Intrusion Detection Algorithm for Network Traffic Using Convolution Neural Network 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2023 DOI:10.1109/ICECCT56650.2023.10179767

[7]	Analyzing sensitivity of flood susceptible model in a flood plain river basin Geocarto International, 2022 DOI:10.1080/10106049.2021.1967464

[8]	Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal Computers, Materials & Continua, 2022 DOI:10.32604/cmc.2022.028339

[9]	Detection of Traffic on the Network based on a Real Dataset for the IIM method and ML-TSDS Algorithm 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), 2022 DOI:10.1109/ICACRS55517.2022.10029324

[10]	Intelligent One-Class Classifiers for the Development of an Intrusion Detection System: The MQTT Case Study Electronics, 2022 DOI:10.3390/electronics11030422

[11]	Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional convolutional neural networks Applied Intelligence, 2022 DOI:10.1007/s10489-022-03995-2

[12]	An Explainable Deep Neural Framework for Trustworthy Network Intrusion Detection 2022 10th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), 2022 DOI:10.1109/MobileCloud55333.2022.00011

[13]	Online Kanji Characters Based Writer Identification Using Sequential Forward Floating Selection and Support Vector Machine Applied Sciences, 2022 DOI:10.3390/app122010249

[14]	Shaping future low-carbon energy and transportation systems: Digital technologies and applications iEnergy, 2022 DOI:10.23919/IEN.2022.0040

[15]	Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models Scientific Reports, 2022 DOI:10.1038/s41598-022-18273-x

[16]	Balanced Twin Auto-Encoder for IoT Intrusion Detection GLOBECOM 2022 - 2022 IEEE Global Communications Conference, 2022 DOI:10.1109/GLOBECOM48099.2022.10000891

[17]	Proceedings of International Conference on Emerging Technologies and Intelligent Systems Lecture Notes in Networks and Systems, 2022 DOI:10.1007/978-3-030-85990-9_24

[18]	Sustainable Smart Cities and Territories Lecture Notes in Networks and Systems, 2022 DOI:10.1007/978-3-030-78901-5_22

[19]	Contextual anomaly detection on time series: a case study of metro ridership analysis Neural Computing and Applications, 2022 DOI:10.1007/s00521-021-06455-z

[20]	An Abnormal Traffic Detection Model Combined BiIndRNN With Global Attention IEEE Access, 2022 DOI:10.1109/ACCESS.2022.3159550

[21]	Evaluating the Performance of Various SVM Kernel Functions Based on Basic Features Extracted from KDDCUP'99 Dataset by Random Forest Method for Detecting DDoS Attacks Wireless Personal Communications, 2022 DOI:10.1007/s11277-021-09280-8

[22]	Important Features Selection and Classification of Adult and Child from Handwriting Using Machine Learning Methods Applied Sciences, 2022 DOI:10.3390/app12105256

[23]	Network traffic verification based on a public dataset for IDS systems and machine learning classification algorithms 2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), 2022 DOI:10.23919/MIPRO55190.2022.9803674

[24]	Intrusion Detection System Using K-Means and Edited Nearest Neighbour Algorithm International Journal of Advanced Research in Science, Communication and Technology, 2022 DOI:10.48175/IJARSCT-5052

[25]	Assessing the Yield of Wheat Using Satellite Remote Sensing-Based Machine Learning Algorithms and Simulation Modeling Remote Sensing, 2022 DOI:10.3390/rs14133005

[26]	A statistical analysis of intrinsic bias of network security datasets for training machine learning mechanisms Annals of Telecommunications, 2022 DOI:10.1007/s12243-021-00904-5

[27]	Depression Anxiety Stress Scale and Handgrip using Machine Learning Analysis 2022 4th International Conference on Smart Sensors and Application (ICSSA), 2022 DOI:10.1109/ICSSA54161.2022.9870948

[28]	RADE: resource-efficient supervised anomaly detection using decision tree-based ensemble methods Machine Learning, 2021 DOI:10.1007/s10994-021-06047-x

[29]	Network Security: Threat Model, Attacks, and IDS Using Machine Learning 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021 DOI:10.1109/ICAIS50930.2021.9395884

[30]	A Powerful Ensemble Learning Approach for Improving Network Intrusion Detection System (NIDS) 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), 2021 DOI:10.1109/ICDS53782.2021.9626727

[31]	A Tree Based Machine Learning and Deep Learning Classification for Network Intrusion Detection European Journal of Science and Technology, 2021 DOI:10.31590/ejosat.889994

[32]	ACGANs-CNN: A Novel Intrusion Detection Method Journal of Physics: Conference Series, 2021 DOI:10.1088/1742-6596/1757/1/012012

[33]	Intrusion Detection of Imbalanced Network Traffic Based on Machine Learning and Deep Learning IEEE Access, 2021 DOI:10.1109/ACCESS.2020.3048198

[34]	Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms Crystals, 2021 DOI:10.3390/cryst11040324

[35]	Resource Efficient Boosting Method for IoT Security Monitoring 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), 2021 DOI:10.1109/CCNC49032.2021.9369620

[36]	Computer Networks, Big Data and IoT Lecture Notes on Data Engineering and Communications Technologies, 2021 DOI:10.1007/978-981-16-0965-7_67

[37]	Machine Learning for Threat Recognition in Critical Cyber-Physical Systems 2021 IEEE International Conference on Cyber Security and Resilience (CSR), 2021 DOI:10.1109/CSR51186.2021.9527979

[38]	Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning Sensors, 2021 DOI:10.3390/s21196407

[39]	Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection 2021 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI), 2021 DOI:10.1109/CCCI52664.2021.9583206

[40]	IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection Electronics, 2021 DOI:10.3390/electronics10222857

[41]	Second International Conference on Computer Networks and Communication Technologies Lecture Notes on Data Engineering and Communications Technologies, 2020 DOI:10.1007/978-3-030-37051-0_95

[42]	Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction International Journal of Production Research, 2020 DOI:10.1080/00207543.2020.1741716

[43]	IGAN-IDS: An Imbalanced Generative Adversarial Network towards Intrusion Detection System in Ad-hoc Networks Ad Hoc Networks, 2020 DOI:10.1016/j.adhoc.2020.102177

[44]	A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms Symmetry, 2020 DOI:10.3390/sym12061046

[45]	Computational method to prove efficacy of datasets Journal of Information and Optimization Sciences, 2020 DOI:10.1080/02522667.2020.1747193

[46]	An Enhanced Way of Distributed Denial of Service Attack Detection by Applying Machine Learning Algorithms in Cloud Computing Journal of Computational and Theoretical Nanoscience, 2020 DOI:10.1166/jctn.2020.9317

[47]	Assessment of Machine Learning Techniques for Building an Efficient IDS 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), 2020 DOI:10.1109/SMART-TECH49988.2020.00048

[48]	A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), 2020 DOI:10.1109/WIECON-ECE52138.2020.9397945

[49]	An Intrusion Detection Model based on a Convolutional Neural Network Journal of Multimedia Information System, 2019 DOI:10.33851/JMIS.2019.6.4.165

[50]	Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc Networks Journal of Physics: Conference Series, 2019 DOI:10.1088/1742-6596/1288/1/012075

[51]	Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study Applied Sciences, 2019 DOI:10.3390/app9163322

[52]	A Novel Intrusion Detector Based on Deep Learning Hybrid Methods 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), 2019 DOI:10.1109/BigDataSecurity-HPSC-IDS.2019.00062

[53]	Exploration of Cervical Myelopathy Location From Somatosensory Evoked Potentials Using Random Forests Classification IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019 DOI:10.1109/TNSRE.2019.2945634

[54]	HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection IEEE Access, 2018 DOI:10.1109/ACCESS.2017.2780250

[55]	Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning IEEE Access, 2018 DOI:10.1109/ACCESS.2017.2782159

[56]	LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network Security and Communication Networks, 2018 DOI:10.1155/2018/6026878

[57]	Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors Sensors, 2018 DOI:10.3390/s18124132

[58]	An Abnormal Behavior Detection Based on Deep Learning 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), 2018 DOI:10.1109/SmartWorld.2018.00045

[59]	DoS Attack Detection Using Machine Learning and Neural Network 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018 DOI:10.1109/ICCUBEA.2018.8697702

[60]	Securing Big Data Ecosystem with NSGA-II and Gradient Boosted Trees Based NIDS Using Spark 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), 2018 DOI:10.1109/ICCONS.2018.8663120

[61]	NIDS: Neural Network based Intrusion Detection System 2018 IEEE International Symposium on Technologies for Homeland Security (HST), 2018 DOI:10.1109/THS.2018.8574174

[62]	Towards a multi-layers anomaly detection framework for analyzing network traffic Concurrency and Computation: Practice and Experience, 2017 DOI:10.1002/cpe.3955

[63]	Protein subcellular localization prediction using multiple kernel learning based support vector machine Mol. BioSyst., 2017 DOI:10.1039/C6MB00860G

[64]	A Novel Unsupervised Anomaly Detection Approach for Intrusion Detection System 2017 IEEE 3rd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 2017 DOI:10.1109/BigDataSecurity.2017.56

[65]	Study on credit evaluation of electricity users based on random forest 2017 Chinese Automation Congress (CAC), 2017 DOI:10.1109/CAC.2017.8243614

[66]	Random Forest Modeling for Network Intrusion Detection System Procedia Computer Science, 2016 DOI:10.1016/j.procs.2016.06.047

[67]	Feature Selection for Intrusion Detection Using Random Forest Journal of Information Security, 2016 DOI:10.4236/jis.2016.73009

[68]	Behaviour-Based Attack Detection and Classification in Cyber Physical Systems Using Machine Learning Proceedings of the 2nd ACM International Workshop on Cyber-Physical System Security - CPSS '16, 2016 DOI:10.1145/2899015.2899016

[69]	Comparative study for feature selection algorithms in intrusion detection system Automatic Control and Computer Sciences, 2016 DOI:10.3103/S0146411616010028

[70]	Applying Big Data Analytics Into Network Security: Challenges, Techniques and Outlooks 2016 IEEE International Conference on Smart Cloud (SmartCloud), 2016 DOI:10.1109/SmartCloud.2016.62

[71]	A Novel Anomaly Detection Approach for Mitigating Web-Based Attacks Against Clouds 2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing, 2015 DOI:10.1109/CSCloud.2015.46

[72]	MARK-ELM: Application of a novel Multiple Kernel Learning framework for improving the robustness of Network Intrusion Detection Expert Systems with Applications, 2015 DOI:10.1016/j.eswa.2014.12.040

[73]	Hybrid ModifiedK-Means with C4.5 for Intrusion Detection Systems in Multiagent Systems The Scientific World Journal, 2015 DOI:10.1155/2015/294761

[74]	An information hiding system based on high frame rate video 2015 International Conference on Computers, Communications, and Systems (ICCCS), 2015 DOI:10.1109/CCOMS.2015.7562890

[75]	An evolutionary fuzzy genetic and Naïve Bayesian approach for multivariate data classification 2014 17th International Conference on Computer and Information Technology (ICCIT), 2014 DOI:10.1109/ICCITechn.2014.7073108

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

	Paper Publishing WeChat

Journal of Intelligent Learning Systems and Applications

Journal of Intelligent Learning Systems and Applications

Journals Menu

Home

About SCIRP

Service

Policies