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Hybrid intelligent systems for detecting network intrusions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11424826_27openalex-snapshot
Neural Network Techniques for Host Anomaly Intrusion Detection Using Fixed Pattern Transformation
inferredcites →https://doi.org/10.1007/11527503_82openalex-snapshot
An Adaptive Network Intrusion Detection Method Based on PCA and Support Vector Machines
inferredcites →https://doi.org/10.1007/11569596_25openalex-snapshot
SVM Approach with a Genetic Algorithm for Network Intrusion Detection
inferredcites →https://doi.org/10.1007/3-540-39945-3_6openalex-snapshot
Adaptive, Model-Based Monitoring for Cyber Attack Detection
inferredcites →https://doi.org/10.1007/978-0-387-44641-7_56openalex-snapshot
Network Anomalous Intrusion Detection using Fuzzy-Bayes
inferredcites →https://doi.org/10.1007/978-0-85729-504-0_15openalex-snapshot
Network Intrusion Detection
inferredcites →https://doi.org/10.1007/978-3-540-45248-5_13openalex-snapshot
An Analysis of the 1999 DARPA/Lincoln Laboratory Evaluation Data for Network Anomaly Detection
inferredcites →https://doi.org/10.1007/978-3-642-10677-4_70openalex-snapshot
Semi-Naïve Bayesian Method for Network Intrusion Detection System
inferredcites →https://doi.org/10.1007/bf00114779openalex-snapshot
A Nearest Hyperrectangle Learning Method
inferredcites →https://doi.org/10.1007/bf00114780openalex-snapshot
Conflict Resolution as Discovery in Particle Physics
inferredcites →https://doi.org/10.1016/0306-4379(91)90042-8openalex-snapshot
IFIP—international federation for information processing
inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50023-2openalex-snapshot
Fast Effective Rule Induction
inferredcites →https://doi.org/10.1016/c2009-0-19715-5openalex-snapshot
Data Mining: Practical Machine Learning Tools and Techniques
inferredcites →https://doi.org/10.1016/j.eswa.2016.07.024openalex-snapshot
A hash-based co-clustering algorithm for categorical data
inferredcites →https://doi.org/10.1016/j.ins.2007.03.025openalex-snapshot
A hybrid machine learning approach to network anomaly detection
inferredcites →https://doi.org/10.1016/j.ins.2007.11.028openalex-snapshot
A hybrid artificial immune system and Self Organising Map for network intrusion detection
inferredcites →https://doi.org/10.1016/j.ins.2008.10.025openalex-snapshot
Multiple criteria mathematical programming for multi-class classification and application in network intrusion detection
inferredcites →https://doi.org/10.1016/j.ins.2010.02.023openalex-snapshot
On the versatility of radial basis function neural networks: A case study in the field of intrusion detection
inferredcites →https://doi.org/10.1016/j.jnca.2004.01.003openalex-snapshot
Intrusion detection using an ensemble of intelligent paradigms
inferredcites →https://doi.org/10.1109/artcom.2009.121openalex-snapshot
Ensembling Rule Based Classifiers for Detecting Network Intrusions
inferredcites →https://doi.org/10.1109/cisda.2009.5356528openalex-snapshot
A detailed analysis of the KDD CUP 99 data set
inferredcites →https://doi.org/10.1142/9781848163874_0006openalex-snapshot
MINING KNOWLEDGE FROM NETWORK INTRUSION DATA USING DATA MINING TECHNIQUES
inferredcites →https://doi.org/10.1145/382912.382923openalex-snapshot
Testing Intrusion detection systems
inferredcites →https://doi.org/10.1145/508791.508835openalex-snapshot
Service specific anomaly detection for network intrusion detection
inferredcites →https://doi.org/10.1201/9780429332821openalex-snapshot
Proceedings of The 17th International Symposium on Computer and Information Sciences
inferredcites →https://doi.org/10.1515/jisys.2009.18.3.229openalex-snapshot
A Hybrid Clustering Approach for Network Intrusion Detection Using Cobweb and FFT
inferredcites →https://doi.org/10.48550/arxiv.0911.0485openalex-snapshot
Novel Intrusion Detection using Probabilistic Neural Network and Adaptive Boosting
inferredcites →https://doi.org/10.5121/ijnsa.2010.2202openalex-snapshot
Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/sec.1547openalex-snapshot
Detection and classification of anomaly intrusion using hierarchy clustering and SVM
inferred← citeshttps://doi.org/10.1007/978-3-319-65188-0_4openalex-snapshot
Network Traffic Anomaly Detection Techniques and Systems
inferred← citeshttps://doi.org/10.1007/s10586-024-05060-8openalex-snapshot
HCIVAD: Explainable hybrid voting classifier for network intrusion detection systems
inferred← citeshttps://doi.org/10.1007/s13042-024-02187-3openalex-snapshot
HFCCW: A Novel Hybrid Filter-Clustering-Coevolutionary Wrapper Feature Selection Approach for Network Anomaly Detection
inferred← citeshttps://doi.org/10.1007/s13369-018-3507-5openalex-snapshot
Integrated Intrusion Detection Model Using Chi-Square Feature Selection and Ensemble of Classifiers
inferred← citeshttps://doi.org/10.1007/s44163-026-01217-zopenalex-snapshot
The effect of feature reduction on an ANFIS-based intrusion detection system
inferred← citeshttps://doi.org/10.1016/j.chaos.2018.03.010openalex-snapshot
Nearest neighbors based density peaks approach to intrusion detection
inferred← citeshttps://doi.org/10.1016/j.dcan.2020.06.001openalex-snapshot
Clustering-based label estimation for network anomaly detection
inferred← citeshttps://doi.org/10.1016/j.ijleo.2022.170079openalex-snapshot
RETRACTED: Fusing network traffic features with host traffic features for an improved 5G network intrusion detection system
inferred← citeshttps://doi.org/10.1016/j.jnca.2017.04.015openalex-snapshot
Stealthy Denial of Service (DoS) attack modelling and detection for HTTP/2 services
inferred← citeshttps://doi.org/10.1051/e3sconf/202128009005openalex-snapshot
Predicting anomaly conditions of energy equipment using neural networks
inferred← citeshttps://doi.org/10.1080/15536548.2016.1257680openalex-snapshot
Ca-NIDS: A network intrusion detection system using combinatorial algorithm approach
inferred← citeshttps://doi.org/10.1109/access.2019.2943249openalex-snapshot
An Empirical Evaluation of Deep Learning for Network Anomaly Detection
inferred← citeshttps://doi.org/10.1109/conielecomp.2018.8327205openalex-snapshot
Classification of network anomalies in flow level network traffic using Bayesian networks
inferred← citeshttps://doi.org/10.1109/icaiot57170.2022.10121889openalex-snapshot
Advanced Frameworks Human-Type Communication Over Wireless Sensor Network
inferred← citeshttps://doi.org/10.1109/icait47043.2019.8987368openalex-snapshot
Detection of Network Intrusions using Hybrid Intelligent Systems
inferred← citeshttps://doi.org/10.1109/imis.2015.43openalex-snapshot
Multi-layer Anomaly Detection for Internet Traffic Based on Data Mining
inferred← citeshttps://doi.org/10.1109/iri.2018.00023openalex-snapshot
Utilizing Netflow Data to Detect Slow Read Attacks
inferred← citeshttps://doi.org/10.1109/mticti53925.2021.9664776openalex-snapshot
A Survey on Intrusion Detection System in Ad Hoc Networks Based on Machine Learning
inferred← citeshttps://doi.org/10.1109/surv.2013.052213.00046openalex-snapshot
Network Anomaly Detection: Methods, Systems and Tools
inferred← citeshttps://doi.org/10.1109/usbereit48449.2020.9117692openalex-snapshot
Automated Process Control Anomaly Detection Using Machine Learning Methods
inferred← citeshttps://doi.org/10.1145/3313778openalex-snapshot
A Semi-Boosted Nested Model With Sensitivity-Based Weighted Binarization for Multi-Domain Network Intrusion Detection
inferred← citeshttps://doi.org/10.1155/2016/3943859openalex-snapshot
A Multiple Classifier Fusion Algorithm Using Weighted Decision Templates
inferred← citeshttps://doi.org/10.1155/2022/3794579openalex-snapshot
Application of a New Feature Generation Algorithm in Intrusion Detection System
inferred← citeshttps://doi.org/10.18201/ijisae.2017534722openalex-snapshot
Comparison of Classification Techniques on Energy Efficiency Dataset
inferred← citeshttps://doi.org/10.18280/isi.270118openalex-snapshot
Effective Intrusion Detection System Using Classifier Ensembles
inferred← citeshttps://doi.org/10.20533/jitst.2046.3723.2023.0099openalex-snapshot
Machine Learning-Based Model for Intrusion Detection System
inferred← citeshttps://doi.org/10.26102/2310-6018/2021.34.3.019openalex-snapshot
Hybrid intelligent intrusion detection system based on combining machine learning methods
inferred← citeshttps://doi.org/10.3390/app15041903openalex-snapshot
Deep Learning vs. Machine Learning for Intrusion Detection in Computer Networks: A Comparative Study
inferred← citeshttps://doi.org/10.3390/info10110356openalex-snapshot
An Intrusion Detection System Based on a Simplified Residual Network
inferred← citeshttps://doi.org/10.3390/s20092559openalex-snapshot
The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems
inferred← citeshttps://doi.org/10.4018/978-1-4666-4940-8.ch005openalex-snapshot
Characterizing Intelligent Intrusion Detection and Prevention Systems Using Data Mining
inferred← citeshttps://doi.org/10.53759/181x/jcns202202019openalex-snapshot
An Review of Medical Cyber Physical System Architecture and STP Issues
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