scholarly journals Assessment of Machine Learning Algorithms for Network Intrusion Detection

2020 ◽  
Vol 9 (4) ◽  
pp. 1667-1671
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
Learning Models ◽  
Network Intrusion ◽  
Tree Classifier ◽  
Machine Learning Models

A Network Intrusion Detection System (NIDS) is a framework to identify network interruptions as well as abuse by checking network traffic movement and classifying it as either typical or strange. Numerous Intrusion Detection Systems have been implemented using simulated datasets like KDD’99 intrusion dataset but none of them uses a real time dataset. The proposed work performs and assesses tests to overview distinctive machine learning models reliant on KDD’99 intrusion dataset and an ongoing created dataset. The machine learning models achieved to compute required performance metrics so as to assess the chosen classifiers. The emphasis was on the accuracy metric so as to improve the recognition pace of the interruption identification framework. The actualized calculations showed that the decision tree classifier accomplished the most noteworthy estimation of accuracy while the logistic regression classifier has accomplished the least estimation of exactness for both of the datasets utilized.

A Survey on Data-driven Network Intrusion Detection

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Author(s):  
Dylan Chou ◽  
Meng Jiang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Data Driven ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Learning Models ◽  
Simulated Environments ◽  
Network Intrusion ◽  
Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

scholarly journals An Efficient Intrusion Detection Method Based on LightGBM and Autoencoder

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1458
Author(s):  
Chaofei Tang ◽  
Nurbol Luktarhan ◽  
Yuxin Zhao
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Reconstruction Error ◽  
Machine Learning Algorithms ◽  
Network Intrusion Detection ◽  
Attack Behavior ◽  
Network Intrusion ◽  
Normal Behavior

Due to the insidious characteristics of network intrusion behaviors, developing an efficient intrusion detection system is still a big challenge, especially in the era of big data where the number of traffic and the dimension of each traffic feature are high. Because of the shortcomings of traditional common machine learning algorithms in network intrusion detection, such as insufficient accuracy, a network intrusion detection system based on LightGBM and autoencoder (AE) is proposed. The LightGBM-AE model proposed in this paper includes three steps: data preprocessing, feature selection, and classification. The LightGBM-AE model adopts the LightGBM algorithm for feature selection, and then uses an autoencoder for training and detection. When a set of data containing network intrusion behaviors are inputted into an autoencoder, there is a large reconstruction error between the original input data and the reconstructed data obtained by the autoencoder, which provides a basis for intrusion detection. According to the reconstruction error, an appropriate threshold is set to distinguish symmetrically between normal behavior and attack behavior. The experiment is carried out on the NSL-KDD dataset and implemented using Pytorch. In addition to autoencoder, variational autoencoder (VAE) and denoising autoencoder (DAE) are also used for intrusion detection and are compared with existing machine learning algorithms such as Decision Tree, Random Forest, KNN, GBDT, and XGBoost. The evaluation is carried out through classification evaluation indexes such as accuracy, precision, recall, F1-score. The experimental results show that the method can efficiently separate the attack behavior from normal behavior according to the reconstruction error. Compared with other methods, the effectiveness and superiority of this method are verified.

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