A Feed-Forward and Pattern Recognition ANN Model for Network Intrusion Detection
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Summary
Feed Forward Neural Network and Pattern Recognition Neural Network are designed and tested for the detection of various attacks by using modified KDD Cup99 dataset and results have shown that both the models have outperformed each other in different performance measures on different attack detections.
- Type
- article
- Published
- 2019-04-08
- Cited by
- 42
- References
- 37
- Access
- Open access
- OpenAlex
- https://openalex.org/W2936995087
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:146033237
Keywords
Computer science, Artificial neural network, Intrusion detection system, Conjugate gradient method, Data mining
References
- KDD Feature Set Complaint Heuristic Rules for R2L Attack Detection
- A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning
- Effect of training algorithms on neural networks aided pavement diagnosis
- Testing Intrusion detection systems
- Comparison of artificial neural networks (ANN) with classical modelling techniques using different experimental designs and data from a galenical study on a solid dosage form.
- An Implementation of Intrusion Detection System Using Genetic Algorithm
- Enhancing Big Data Security with Collaborative Intrusion Detection
- Efficient deterministic method for detecting new U2R attacks
- Mean squared error of empirical predictor
- Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm
- An introduction to ROC analysis
- Introduction To Intrusion Detection System: Review
- Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm
- The Use of Intelligent Algorithms to Detect Attacks In Intrusion Detection System
- A Novel Approach for Efficient Usage of Intrusion Detection System in Mobile Ad Hoc Networks
- Data Randomization and Cluster-Based Partitioning for Botnet Intrusion Detection
- Multidimensional Intrusion Detection System for IEC 61850-Based SCADA Networks
- Suspicious Flow Forwarding for Multiple Intrusion Detection Systems on Software-Defined Networks
- Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model
- Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Cited by
- Performance Analysis of Machine Learning Techniques on Software Defect Prediction using NASA Datasets
- A Framework for Software Defect Prediction Using Feature Selection and Ensemble Learning Techniques
- A Feature Selection based Ensemble Classification Framework for Software Defect Prediction
- Performance Analysis of Resampling Techniques on Class Imbalance Issue in Software Defect Prediction
- A Classification Framework for Software Defect Prediction Using Multi-filter Feature Selection Technique and MLP
- A Classification Framework to Detect DoS Attacks
- Prediction of Defect Prone Software Modules using MLP based Ensemble Techniques
- Network intrusion detection using multi-architectural modular deep neural network
- Intrusion Detection System using Machine Learning Techniques: A Review
- Software Defect Prediction Using Variant based Ensemble Learning and Feature Selection Techniques
- Efficient Redundancy Processing Framework of Association Rules Model based on Hypergraph in Information Pattern Recognition
- Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets
- Development of a Small Intelligent Weather Station for Agricultural Applications
- A Review on Intrusion Detection System using Machine Learning Techniques
- Software Defect Prediction Using Supervised Machine Learning Techniques: A Systematic Literature Review
- An Enhanced Intrusion Detection System using Particle Swarm Optimization Feature Extraction Technique
- COVID-19 Detection from CBC using Machine Learning Techniques
- Treatment Response Prediction in Hepatitis C Patients using Machine Learning Techniques
- Modelling DDoS Attacks in IoT Networks using Machine Learning
- Detailed Study of Wine Dataset and its Optimization
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