Journal of Intelligent Learning Systems and Applications

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
"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):
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[1] Resource Efficient Boosting Method for IoT Security Monitoring
2021
[2] Intelligent Machine Learning Approach for CIDS—Cloud Intrusion Detection System
2021
[3] Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms
2021
[4] A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods
2021
[5] Forecast and anomaly detection on time series with dynamic context. Application to the mining of transit ridership data.
2021
[6] DS-kNN: An Intrusion Detection System Based on a Distance Sum-Based K-Nearest Neighbors
2021
[7] ACGANs-CNN: A Novel Intrusion Detection Method
2021
[8] Assessment of Machine Learning Techniques for Building an Efficient IDS
2020
[9] A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine
2020
[10] A Semi-supervised Intrusion Detection Algorithm Based on Auto-encoder
2020
[11] Retracted: DETECTING ATTACKS ON MQTT-IOT PROTOCOL USING ML TECHNIQUES
2020
[12] Reliable Forest Fire Detection System Using Wireless Sensor Networks and Internet of Things
2020
[13] Using Parametric t-Distributed Stochastic Neighbor Embedding Combined with Hierarchical Neural Network for Network Intrusion Detection.
2020
[14] A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms
2020
[15] Intrusion detection of imbalanced network traffic based on machine learning and deep learning
2020
[16] Attackers are not stealthy: Statistical analysis of the well-known and infamous KDD network security dataset
2020
[17] Predictor selection and attack classification using random forest for intrusion detection
2020
[18] A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms
2020
[19] Computational method to prove efficacy of datasets
2020
[20] IGAN-IDS: An Imbalanced Generative Adversarial Network towards Intrusion Detection System in Ad-hoc Networks
2020
[21] Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction
2020
[22] Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
2020
[23] Machine Learning Method for Cyber Security Intrusion Detection for Industrial Control Systems (ICSS)
2020
[24] Tuning to Optimize SVM Approach for Breast Cancer Diagnosis with Blood Analysis Data
2020
[25] Dimensionality Reduction for DDOS Database Using PCA
2020
[26] Systematic Literature Survey on IDS Based on Data Mining
2019
[27] Improving Situation Awareness Through Monitoring Data Correlation
2019
[28] SIGMA: Strengthening IDS with GAN and Metaheuristics Attacks
2019
[29] A Machine Learning Approach for Network Traffic Analysis using Random Forest Regression
ACET Journal of Computer Education & Research, 2019
[30] Fuzzy Automaton-based Early Detection Model
2019
[31] An Intrusion Detection Model based on a Convolutional Neural Network
2019
[32] Exploration of Cervical Myelopathy Location From Somatosensory Evoked Potentials Using Random Forests Classification
2019
[33] Intensive Pre-Processing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
2019
[34] Ensemble-based semi-supervised learning approach for a distributed intrusion detection system
2019
[35] Rade: Resource-efficient supervised anomaly detection using decision tree-based ensemble methods
2019
[36] Hybrid Architecture for Distributed Intrusion Detection System.
2019
[37] Multiclass classification procedure for detecting attacks on MQTT-IoT protocol
2019
[38] A Novel Intrusion Detector Based on Deep Learning Hybrid Methods
2019
[39] Analysis of NSL KDD Dataset Using Classification Algorithms for Intrusion Detection System
2019
[40] A Data-Driven Network Intrusion Detection Model Based on Host Clustering and Integrated Learning: A Case Study on Botnet Detection
2019
[41] Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc Networks
2019
[42] Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study
2019
[43] Hybrid Architecture for Distributed Intrusion Detection System
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[44] ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features
2019
[45] Coupled Kernel Ensemble Regression
International Journal of Computer Applications, 2018
[46] Securing Big Data Ecosystem with NSGA-II and Gradient Boosted Trees Based NIDS Using Spark
2018
[47] Comparison of Performance Between Incremental and Batch Learning Method for Information Analysis of Cyber Surveillance and Reconnaissance
2018
[48] DoS Attack Detection Using Machine Learning and Neural Network
2018
[49] Co-regularized kernel ensemble regression
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[50] Semi-Supervised Machine Learning for Network Intrusion Detection
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[51] Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems
2018
[52] NIDS: Neural Network based Intrusion Detection System
2018
[53] An Abnormal Behavior Detection Based on Deep Learning
2018
[54] Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
2018
[55] Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
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[56] An Effective Way of Cloud Intrusion Detection System Using Decision tree, Support Vector Machine and Naïve Bayes Algorithm
2018
[57] Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning
2018
[58] Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
2018
[59] WebAD: A Cascading Model Based on Machine Learning for Web Attacks Detection
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[60] A novel method combining fuzzy SVM and sampling for imbalanced classification
International Journal of Applied Systemic Studies, 2018
[61] LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network
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[62] Network Intrusion Detection on Apache Spark with Machine Learning Algorithms
Engineering Applications of Neural Networks, 2018
[63] Significant Metabolites and Outlier-Robust Classifier Identification for Breast Cancer Prediction
Current Metabolomics, 2018
[64] Classifying Building Usages: A Machine Learning Approach on Building Extractions
2018
[65] iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines
2017
[66] Prediction of protein subcellular localization using support vector machine with the choice of proper kernel
2017
[67] Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques
2017
[68] Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques تاينقت مادختساب تايفشتسملا يف ةعشلأا ءابطأ ةيجاتنإ نيسحت بيقنت …‎
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[69] Protein subcellular localization prediction using multiple kernel learning based support vector machine
Molecular BioSystems, 2017
[70] iMulti-HumPhos: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins Using Multiple Kernel Learning Based Support Vector Machine
Molecular BioSystems, 2017
[71] A Novel Unsupervised Anomaly Detection Approach for Intrusion Detection System
2017
[72] HAST-IDS: Learning Hierarchical Spatial-Temporal Features using Deep Neural Networks to Improve Intrusion Detection
2017
[73] Semi-supervised Random Forest for Intrusion Detection Network
MAICS, 2017
[74] A novel network security algorithm based on improved support vector machine from smart city perspective
Computers & Electrical Engineering, 2017
[75] Multi-Task Learning for Intrusion Detection on web logs
Journal of Systems Architecture, 2017
[76] A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility
CATENA, 2017
[77] predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue
Analytical Biochemistry, 2017
[78] Cyber-physical intrusion detection for robotic vehicles
2017
[79] Study on credit evaluation of electricity users based on random forest
2017
[80] Identifying Intrusions in Computer Networks using Principal Component Analysis and Random Forest
International Journal of Computer Science and Information Security, 2016
[81] Semi-Supervised Deep Neural Network for Network Intrusion Detection
2016
[82] Relationship between Effective Application of Machine Learning and Malware Detection: A Quantitative Study
ProQuest Dissertations Publishing, 2016
[83] Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
2016
[84] Towards a multi‐layers anomaly detection framework for analyzing network traffic
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[85] Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems
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[86] A Granular Classifier By Means of Context-based Similarity Clustering
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[87] Anomaly Detection with ANN and SVM for Telemedicine Networks
International Journal of Computer, Electrical, Automation, Control and Information Engineering, 2016
[88] A decision tree-based rule formation with combined PSO-GA algorithm for intrusion detection system
International Journal of Internet Technology and Secured Transactions, 2016
[89] Random forest modeling for network intrusion detection system
Procedia Computer Science, 2016
[90] Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems.
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[91] Applying Big Data Analytics Into Network Security: Challenges, Techniques and Outlooks
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[92] A Novel Anomaly Detection Approach for Mitigating Web-Based Attacks Against Clouds
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[93] 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
[94] Hybrid Modified-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
The Scientific World Journal, 2015
[95] An information hiding system based on high frame rate video
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[96] Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection
ProQuest Dissertations Publishing, 2015
[97] Hybrid Modified 𝐾-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
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[98] An Integrated Approach for Intrusion Detection using Computational Methods
Indian Journal of Science and Technology, 2015
[99] An evolutionary fuzzy genetic and Naïve Bayesian approach for multivariate data classification
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[100] An Evolutionary Fuzzy Genetic and Na?ve Bayesian Approach for Multivariate Data Classification
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[101] MARK-ELM: Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection
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[102] Hybrid Architecture for Distributed Intrusion Detection System Using Semi-supervised Classifiers in Ensemble Approach Hybrid Architecture for Distributed …
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