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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[48] RANet: Network intrusion detection with group-gating convolutional neural network
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[51] Intelligent One-Class Classifiers for the Development of an Intrusion Detection System: The MQTT Case Study
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[52] SCADA Vulnerabilities and Attacks: A Review of the State-of-the-Art and Open Issues
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[53] An Abnormal Traffic Detection Model Combined BiIndRNN With Global Attention
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[54] Network intrusion detection based on conditional wasserstein variational autoencoder with generative adversarial network and one-dimensional …
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[55] An Explainable Deep Neural Framework for Trustworthy Network Intrusion Detection
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[56] Network traffic verification based on a public dataset for IDS systems and machine learning classification algorithms
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[57] Evaluating the Performance of Various SVM Kernel Functions Based on Basic Features Extracted from KDDCUP'99 Dataset by Random Forest Method for Detecting …
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[58] Ensemble learning-based IDS for sensors telemetry data in IoT networks
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[59] Balanced twin auto-encoder for iot intrusion detection
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[60] Wearable Internet of Medical Things (IoMT) 5G Secure Remote Monitoring System Model in Smart Cities
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[61] Novel Machine-Learning-Based Decision Support System for Fraud Prevention
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[62] Detection of Traffic on the Network based on a Real Dataset for the IIM method and ML-TSDS Algorithm
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[64] Efficient Feature Selection and Machine Learning Based ADHD Detection Using EEG Signal
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[65] Forecast and anomaly detection on time series with dynamic context: Application to the mining of transit ridership data
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[66] A Review on Intrusion Detection System Using a Machine Learning Algorithms
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[67] Comparing Methods of Feature Extraction of Brain Activities for Octave Illusion Classification Using Machine Learning
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[68] Assessment of temporal probability for rainfall-induced landslides based on nonstationary extreme value analysis
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[69] ACGANs-CNN: A Novel Intrusion Detection Method
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[70] A new approach to monitor water quality in the Menor sea (Spain) using satellite data and machine learning methods
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[71] Forecast and anomaly detection on time series with dynamic context. Application to the mining of transit ridership data.
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[72] DS-kNN: An Intrusion Detection System Based on a Distance Sum-Based K-Nearest Neighbors
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[73] Resource Efficient Boosting Method for IoT Security Monitoring
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[74] Intelligent Machine Learning Approach for CIDS—Cloud Intrusion Detection System
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[75] Recent Advances in the Prediction of Protein Structural Classes: Feature Descriptors and Machine Learning Algorithms
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[76] Intrusion Detection System for MQTT Protocol Based on Intelligent One-Class Classifiers
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[77] Machine Learning for Threat Recognition in Critical Cyber-Physical Systems
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[78] A Tree Based Machine Learning and Deep Learning Classification for Network Intrusion Detection
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[79] Intrusion Detection System Based on RF-SVM Model Optimized with Feature Selection
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[80] A Powerful Ensemble Learning Approach for Improving Network Intrusion Detection System (NIDS)
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[81] IoT Dataset Validation Using Machine Learning Techniques for Traffic Anomaly Detection
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[82] Transferability of Intrusion Detection Systems Using Machine Learning between Networks
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[83] A Machine Learning Approach for Uniform Intrusion Detection
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[84] Identificação das Dunas do Atacama (Norte do Chile) a partir da avaliação de três algoritmos no Google Earth Engine
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[85] PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks
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[86] Anomaly Detection in Network Intrusion Detection Systems Using Machine Learning and Dimensionality Reduction
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[87] Advances in Modelling and Analysis B
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[88] Retracted: DETECTING ATTACKS ON MQTT-IOT PROTOCOL USING ML TECHNIQUES
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[89] Reliable Forest Fire Detection System Using Wireless Sensor Networks and Internet of Things
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[90] Using Parametric t-Distributed Stochastic Neighbor Embedding Combined with Hierarchical Neural Network for Network Intrusion Detection.
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[91] A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms
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[92] Intrusion detection of imbalanced network traffic based on machine learning and deep learning
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[93] Attackers are not stealthy: Statistical analysis of the well-known and infamous KDD network security dataset
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[94] Assessment of Machine Learning Techniques for Building an Efficient IDS
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[95] A Two Layer Machine Learning System for Intrusion Detection Based on Random Forest and Support Vector Machine
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[96] A Semi-supervised Intrusion Detection Algorithm Based on Auto-encoder
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[97] A Comparative Theoretical and Empirical Analysis of Machine Learning Algorithms.
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[98] Predictor selection and attack classification using random forest for intrusion detection
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[99] A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms
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[100] Computational method to prove efficacy of datasets
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[101] IGAN-IDS: An Imbalanced Generative Adversarial Network towards Intrusion Detection System in Ad-hoc Networks
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[102] Learning dispatching rules for single machine scheduling with dynamic arrivals based on decision trees and feature construction
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[103] Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
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[104] Machine Learning Method for Cyber Security Intrusion Detection for Industrial Control Systems (ICSS)
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[108] 基于元优化的 KNN 入侵检测模型.
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[109] Hybrid framework for intrusion detection system using ensemble approach
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[110] Hybrid Architecture for Distributed Intrusion Detection System Using Semisupervised Classifiers in Ensemble Approach
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[112] Improving Situation Awareness Through Monitoring Data Correlation
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[116] An Intrusion Detection Model based on a Convolutional Neural Network
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[118] Intensive Pre-Processing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
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[119] Ensemble-based semi-supervised learning approach for a distributed intrusion detection system
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[120] Rade: Resource-efficient supervised anomaly detection using decision tree-based ensemble methods
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[121] Hybrid Architecture for Distributed Intrusion Detection System.
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[122] Multiclass classification procedure for detecting attacks on MQTT-IoT protocol
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[123] A Novel Intrusion Detector Based on Deep Learning Hybrid Methods
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[124] Analysis of NSL KDD Dataset Using Classification Algorithms for Intrusion Detection System
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[125] A Data-Driven Network Intrusion Detection Model Based on Host Clustering and Integrated Learning: A Case Study on Botnet Detection
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[126] Revisiting Recent and Current Anomaly Detection based on Machine Learning in Ad-Hoc Networks
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[127] Special Issue on Using Machine Learning Algorithms in the Prediction of Kyphosis Disease: A Comparative Study
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[128] Hybrid Architecture for Distributed Intrusion Detection System
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[129] ANID-SEoKELM: Adaptive network intrusion detection based on selective ensemble of kernel ELMs with random features
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[130] Apache spark ve makine öğrenmesi algoritmaları ile ağ saldırısı tespiti
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[131] Classifying Building Usages: A Machine Learning Approach on Building Extractions
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[132] Coupled Kernel Ensemble Regression
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[133] Securing Big Data Ecosystem with NSGA-II and Gradient Boosted Trees Based NIDS Using Spark
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[134] Comparison of Performance Between Incremental and Batch Learning Method for Information Analysis of Cyber Surveillance and Reconnaissance
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[135] DoS Attack Detection Using Machine Learning and Neural Network
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[136] Co-regularized kernel ensemble regression
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[137] Semi-Supervised Machine Learning for Network Intrusion Detection
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[138] Applying Machine Learning to Advance Cyber Security: Network Based Intrusion Detection Systems
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[139] NIDS: Neural Network based Intrusion Detection System
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[140] An Abnormal Behavior Detection Based on Deep Learning
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[141] Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
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[142] Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors
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[143] An Effective Way of Cloud Intrusion Detection System Using Decision tree, Support Vector Machine and Naïve Bayes Algorithm
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[144] Cloud-based cyber-physical intrusion detection for vehicles using Deep Learning
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[145] Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques
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[146] WebAD: A Cascading Model Based on Machine Learning for Web Attacks Detection
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[147] A novel method combining fuzzy SVM and sampling for imbalanced classification
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[148] LA-GRU: Building Combined Intrusion Detection Model Based on Imbalanced Learning and Gated Recurrent Unit Neural Network
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[149] Network Intrusion Detection on Apache Spark with Machine Learning Algorithms
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[150] Significant Metabolites and Outlier-Robust Classifier Identification for Breast Cancer Prediction
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[151] Machine learning classifiers for network intrusion detection
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[152] Modified stacking ensemble machine learning method for network intrusion detection
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[153] Addressing challenges in big data intrusion detection system using machine learning techniques
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[154] Semi-supervised Random Forest for Intrusion Detection Network.
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[155] iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines
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[156] Prediction of protein subcellular localization using support vector machine with the choice of proper kernel
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[157] Improve Radiologists Productivity in Hospitals Based on Data Mining Techniques
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[160] iMulti-HumPhos: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins Using Multiple Kernel Learning Based Support Vector Machine
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[161] A Novel Unsupervised Anomaly Detection Approach for Intrusion Detection System
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[162] HAST-IDS: Learning Hierarchical Spatial-Temporal Features using Deep Neural Networks to Improve Intrusion Detection
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[163] Semi-supervised Random Forest for Intrusion Detection Network
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[164] A novel network security algorithm based on improved support vector machine from smart city perspective
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[165] Multi-Task Learning for Intrusion Detection on web logs
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[168] Cyber-physical intrusion detection for robotic vehicles
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[170] An approach to develop a hybrid algorithm based on support vector machine and Naive Bayes for anomaly detection
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[172] Multiple Kernel Learning And Its Application In Bioinformatics
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[173] Identifying Intrusions in Computer Networks using Principal Component Analysis and Random Forest
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[174] Semi-Supervised Deep Neural Network for Network Intrusion Detection
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[175] Relationship between Effective Application of Machine Learning and Malware Detection: A Quantitative Study
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[176] Detecting Distributed Denial of Service Attacks Using Data Mining Techniques
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[177] Towards a multi‐layers anomaly detection framework for analyzing network traffic
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[178] Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems
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[179] A Granular Classifier By Means of Context-based Similarity Clustering
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[180] Anomaly Detection with ANN and SVM for Telemedicine Networks
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[181] A decision tree-based rule formation with combined PSO-GA algorithm for intrusion detection system
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[182] Random forest modeling for network intrusion detection system
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[183] Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems.
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[184] Applying Big Data Analytics Into Network Security: Challenges, Techniques and Outlooks
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[185] Performance evaluation of different kernels for support vector machine used in intrusion detection system
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[186] FUZZY BASED SUPPORT VECTOR MACHINE CLASSIFIER WITH WIENER FILTER (FSVM-WF) FOR INTRUSION DETECTION SYSTEM.
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[187] AN APPROACH TO DEVELOP THE HYBRID ALGORITHM BASED ON SUPPORT VECTOR MACHINE AND NAÏVE BAYES FOR ANOMALY DETECTION
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[188] 基于随机森林和加权 K 均值聚类的网络入侵检测系统
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[189] Improved Extreme Learning Machine (IELM) Classifier For Intrusion Detection System
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[194] An anamoly based Intrusion Detection System for mobile ad-hoc networks using genetic algorithm based support vector machine
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[196] An information hiding system based on high frame rate video
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[197] Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection
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[198] Hybrid Modified 𝐾-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
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[199] Research Article Hybrid Modified 𝐾-Means with C4. 5 for Intrusion Detection Systems in Multiagent Systems
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[201] An Integrated Approach for Intrusion Detection using Computational Methods
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[204] MARK-ELM: Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection
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[206] Approach for Strengthening the Network Security Based on Boosting Algorithms: Performance Study
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[210] Análise estatística sobre o conjunto de dados de segurança de redes KDD-99 para o desenvolvimento de um sistema de segurança usando aprendizado …
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