Performance analysis of NSL-KDD dataset using ANN
By B. Ingre, Anamika Yadav. 2015 International Conference on Signal Processing and Communication Engineering Systems, 2015
340 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing
View Paper: https://www.semanticscholar.org/paper/00f7114d88a80a797c7aad8770c33dcb537bf951
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Antonio J. Sánchez-Esguevillas
- Jaime Lloret
- Eunjung Choi
- Yuefei Zhu
- Minsun Shim
- Ji Won Kim
- Hyun Jung Kim
- Jiyeon Kim
In Computer Networks and Communications
- Antonio J. Sánchez-Esguevillas
- Jaime Lloret
- Eunjung Choi
- Yuefei Zhu
- Minsun Shim
- Ji Won Kim
- Hyun Jung Kim
- Jiyeon Kim
In Signal Processing
- Antonio J. Sánchez-Esguevillas
- Jaime Lloret
- Yuefei Zhu
- Jin-long Fei
- Belén Carro
- Chuanlong Yin
- Manuel López Martín
- Yanxia Sun
Citations in the corpus
- Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset (2020, methodology): “The authors in [20] implemented an IDS using a filter-based method methodology with the aim to drop the number of input attributes (features) required to perform the training and testing of their model.”
- Benchmarking of Machine Learning for Anomaly Based Intrusion Detection Systems in the CICIDS2017 Dataset (2021, methodology): “ANN is employed to analyze the NSL-KDD dataset [16].”
- A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks (2017, methodology): “In [21], the authors have shown the results obtained by J48, Naive Bayesian, Random Forest, Multi-layer Perceptron, Support Vector Machine and the other classification algorithms, and the artificial neural network algorithm also gives 81.2% in [13], which is the recent literature about ANN algorithm”
- Effective Intrusion Detection System Using XGBoost (2018, neutral): “ANN (Artificial Neural Network) (binary class) [35] 81.”
- CNN-Based Network Intrusion Detection against Denial-of-Service Attacks (2020, methodology): “[40] employ ANN for intrusion detection using NSL-KDD.”
- Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT (2017, methodology): “In [15], applying a multilayer perceptron (MLP) with three layers to the NSL-KDD dataset, they achieved an accuracy of 79.9% for test data, for a 5-labels intrusion scenario.”