| Title | Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Taher, Kazi Abu, Mohammed Yasin Jisan, Billal, Rahman, Md. Mahbubur |
| Conference Name | 2019 International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST) |
| Keywords | artificial neural network based machine, Artificial neural networks, Classification algorithms, composability, Deep Learning, feature extraction, feature selection, intrusion, intrusion detection success rate, learning (artificial intelligence), machine learning, machine learning algorithms, machine learning techniques, network intrusion detection, network traffic, neural nets, Neural Network, pattern classification, Predictive Metrics, pubcrawl, Resiliency, security of data, Signal processing algorithms, supervised learning algorithm, supervised machine learning technique, support vector machine, Support vector machines, wrapper feature selection |
| Abstract | A novel supervised machine learning system is developed to classify network traffic whether it is malicious or benign. To find the best model considering detection success rate, combination of supervised learning algorithm and feature selection method have been used. Through this study, it is found that Artificial Neural Network (ANN) based machine learning with wrapper feature selection outperform support vector machine (SVM) technique while classifying network traffic. To evaluate the performance, NSL-KDD dataset is used to classify network traffic using SVM and ANN supervised machine learning techniques. Comparative study shows that the proposed model is efficient than other existing models with respect to intrusion detection success rate. |
| DOI | 10.1109/ICREST.2019.8644161 |
| Citation Key | taher_network_2019 |
Network Intrusion Detection using Supervised Machine Learning Technique with Feature Selection