Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks

@article{Dina2022EffectOB,
  title={Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks},
  author={Ayesha S. Dina and Aamir Siddique and Dakshnamoorthy Manivannan},
  journal={IEEE Access},
  year={2022},
  volume={10},
  pages={96731-96747}
}
Attacks on computer networks have increased significantly in recent days, due in part to the availability of sophisticated tools for launching such attacks as well as the thriving underground cyber-crime economy to support it. Over the past several years, researchers in academia and industry used machine learning (ML) techniques to design and implement Intrusion Detection Systems (IDSes) for computer networks. Many of these researchers used datasets collected by various organizations to train… 

Figures and Tables from this paper

References

SHOWING 1-10 OF 71 REFERENCES

Classification Analysis of Intrusion Detection on NSL-KDD Using Machine Learning Algorithms

TLDR
Challenging and popular NSL-KDD dataset for intrusion detection is chosen for performed experiments, where classification and three benchmark machine learning techniques are used in order to determine optimum technique for classification domain.

Exploring Ensemble-Based Class Imbalance Learners for Intrusion Detection in Industrial Control Networks

TLDR
This article provides a framework that compares nine cost-sensitive individual and ensemble models designed specifically for handling imbalanced data, and demonstrates that EasyEnsemble outperformed significantly in comparison to its rivals across the board.

Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization

TLDR
A reliable dataset is produced that contains benign and seven common attack network flows, which meets real world criteria and is publicly avaliable and evaluates the performance of a comprehensive set of network traffic features and machine learning algorithms to indicate the best set of features for detecting the certain attack categories.

An Adaptive Ensemble Machine Learning Model for Intrusion Detection

TLDR
It is proved that the ensemble model effectively improves detection accuracy, and it is found that the quality of data features is an important factor to determine the detection effect.

A Deep Learning Approach for Intrusion Detection Using Recurrent Neural Networks

TLDR
The experimental results show that RNN-IDS is very suitable for modeling a classification model with high accuracy and that its performance is superior to that of traditional machine learning classification methods in both binary and multiclass classification.

An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks

TLDR
This paper highlights several machine learning methods such as k-nearest neighbour (KNN), support vector machine (SVM), decision tree (DT), naive Bayes (NB), random forest (RF), artificial neural network (ANN), and logistic regression (LR) that can be used in IDS.

A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection

TLDR
A targeted literature survey of machine learning (ML) and data processing (DM) strategies for cyber analytics in support of intrusion detection as it applies to wired networks.

Performance analysis of NSL-KDD dataset using ANN

  • B. IngreA. Yadav
  • Computer Science
    2015 International Conference on Signal Processing and Communication Engineering Systems
  • 2015
TLDR
The performance of the proposed scheme has been compared with existing scheme and higher detection rate is achieved in both binary class as well as five class classification problems.

Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection

TLDR
A deep learning model combining the distinct strengths of a Convolutional Neural Network and a Bi-directional LSTM to incorporate learning of spatial and temporal features of the data is proposed and performs better than many state-of-the-art Network Intrusion Detection systems leveraging Machine Learning/Deep Learning models.
...