A Universal High-Performance Correlation Analysis Detection Model and Algorithm for Network Intrusion Detection System

Zhu, Hongliang; Liu, Wenhan; Sun, Maohua; Yang, Xin

doi:10.1155/2017/8439706

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…But these relations are not explicit and there are links among sources, types of attacks, reports, and incidents of the same type of attacks. Finally, the value is precisely the actionable knowledge that we can get from the cyber database from analyzing the quality of the data, automatized process, prediction of incidents, or detecting intrusion in different networks (see [30][31][32]). …”

Section: A Case Studymentioning

confidence: 99%

On Detecting and Removing Superficial Redundancy in Vector Databases

DeCastro-García

Castañeda

Rodríguez

et al. 2018

Mathematical Problems in Engineering

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A mathematical model is proposed in order to obtain an automatized tool to remove any unnecessary data, to compute the level of the redundancy, and to recover the original and filtered database, at any time of the process, in a vector database. This type of database can be modeled as an oriented directed graph. Thus, the database is characterized by an adjacency matrix. Therefore, a record is no longer a row but a matrix. Then, the problem of cleaning redundancies is addressed from a theoretical point of view. Superficial redundancy is measured and filtered by using the 1-norm of a matrix. Algorithms are presented by Python and MapReduce, and a case study of a real cybersecurity database is performed.

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Section: A Case Studymentioning

confidence: 99%

On Detecting and Removing Superficial Redundancy in Vector Databases

DeCastro-García

Castañeda

Rodríguez

et al. 2018

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…Machine learning is a profound nominee candidate to combat network attacks and mitigate intrusion on security of the networks (Almseidin et al, 2017; bhai Gupta & Agrawal, 2020; Iman & Ahmad, 2020). However, there are several challenges need to solved in order to have a secure network (Salih & Abdulazeez, 2021;Zhang et al, 2018;Zhu et al, 2017). In modern communication networks, a huge amount of data is transmitted in different networks, small parts of these data might be a suspicious or dangerous, but the majority are normal or are licensed user's data packets (Galar et al, 2011;Krawczyk, 2016).…”

Section: Introductionmentioning

confidence: 99%

Reducing False Negative Intrusions Rates of Ensemble Machine Learning Model based on Imbalanced Multiclass Datasets

Mohammed¹,

Hussein²

2023

JOWUA

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In spite of the efforts to improve the efficiency of intrusion detection systems based on machine learning algorithms, these systems still need more. The false negative (FN) prediction outcome is of a major priority among other outcomes, when attacks are considered as normal by classifiers. FN outputs are highly a concern issue, especially for multiclass classification, where minor classes have less instances in imbalanced datasets. In this work, three types of well-known imbalanced multiclass classification datasets are used with ensemble machine learning classifiers. The datasets: KDD99, UNSW_NB15, and CICIDS2017 are balanced using different combination of oversampling and under-sampling techniques to improve false negative rates. Suitable performance metrics have been used to obtain significant outputs improvements in all three datasets types using Random Forest classifier. Achieved accuracies are 99.9852% for KDD99, 83.5451% for UNSW_NB15 and 99.8613% for CICIDS2017. The outcomes of the work using the mentioned datasets have been compared with state-of-the-art related works and the results show a clear improvement in false negative rates.

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Optimization of a Comprehensive Sequence Forecasting Framework Based on DAE-LSTM Algorithm

Yong-he

Ren

Zhang

2021

J. Phys.: Conf. Ser.

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Sales forecasting is a multivariate time series forecasting problem. The main challenges of the forecasting task are the high-dimensional influence variables with noise and the complex time series relationships. In this paper, we propose a DAE-LSTM algorithm combined with denoising autoencoder(DAE) and long short-term memory(LSTM) to deal with this problem. The DAE is a non-linear dimensionality reduction method that enhances data robustness. The LSTM is a deep learning algorithm suitable for dealing with multivariate time series problem. Combining the advantages of the above two algorithms, DAE-LSTM is a new multivariate time series prediction algorithm, which transforms unsupervised DAE into supervised data feature extraction method, and adds feature selection function to LSTM algorithm. In addition, in order to build a comprehensive forecasting framework, the hyperparameters of the model are determined by Bayesian optimization method(BOM) and the parameters of the model are solved by adaptive moment estimation(ADAM) optimization algorithm. In the training process, due to the adaptability of the BOM and ADAM, the forecasting model has few parameters that need to be set artificially, only including dimensional scale and time steps determined by data. Finally, the proposed forecasting framework is applied to forecast the sales of online retailers on JD.COM. In comparison with other machine learning algorithms, including multiple linear regression(MLR), support vector regression(SVR), artificial neural network(ANN) and LSTM, the proposed DAE-LSTM achieves the best prediction accuracy.

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A Universal High-Performance Correlation Analysis Detection Model and Algorithm for Network Intrusion Detection System

Cited by 5 publications

References 26 publications

On Detecting and Removing Superficial Redundancy in Vector Databases

On Detecting and Removing Superficial Redundancy in Vector Databases

Reducing False Negative Intrusions Rates of Ensemble Machine Learning Model based on Imbalanced Multiclass Datasets

Optimization of a Comprehensive Sequence Forecasting Framework Based on DAE-LSTM Algorithm

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