A predictive model for network intrusion detection using stacking approach

Rajagopal, Smitha; Kundapur, Poornima Panduranga; Hareesha, K S

doi:10.11591/ijece.v10i3.pp2734-2741

Cited by 9 publications

(9 citation statements)

References 22 publications

(31 reference statements)

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“…The study [12] proposed a cyber-attack detection model using the stacking technique. In their model, the training process uses some machine learning algorithms including Knearest Neighbor (KNN), Decision Tree (DT) and Logistic Vol.…”

Section: Related Workmentioning

confidence: 99%

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Lam¹

2021

IJACSA

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Nowadays, network intrusion detection is an essential problem because cyber-attacks are increasing in both the number and extent of the danger. Network intrusion techniques often use various methods to bypass the oversight of anomaly detection and surveillance systems. This paper proposes to use behavior analysis techniques, machine learning, and deep learning algorithms for the task of detecting network intrusions. The practical and scientific significance of our paper includes two issues: (1) Regarding the process of selecting and extracting features: instead of using typical abnormal behaviors of attacks, this study will use statistical behaviors that are easy to calculate and extract while still ensuring the effectiveness of the method; (2) Regarding the detection process, this study proposes to use the Random Forest (RF) classification algorithm, the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) deep learning model. The experimental results in Section IV have proven that our proposal in this paper is completely correct and reasonable. Based on the results shown in Section IV, this study has provided network surveillance systems with a number of abnormal behaviors as the basis for detecting network intrusions.

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Section: Related Workmentioning

confidence: 99%

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Lam¹

2021

IJACSA

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“…In their experimental section, the research team [13] used these algorithms in turn to classify different cyberattack components in the UNSW-NB15 dataset. In the study [14], the authors proposed a model to detect cyber-attacks using stacking techniques. Accordingly, in the training process of their model, the author uses machine learning algorithms consisting of K-Nearest Neighbors, Decision Tree, and Logistic Regression in order to build a model based on the UNSW-NB15 and UGR'16 datasets.…”

Section: Related Work 21 Cyber-attacks Detection Based On Unsw-nb15 Datasetmentioning

confidence: 99%

Optimization of network traffic anomaly detection using machine learning

Dam

Lam

2021

IJECE

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In this paper, to optimize the process of detecting cyber-attacks, we choose to propose 2 main optimization solutions: Optimizing the detection method and optimizing features. Both of these two optimization solutions are to ensure the aim is to increase accuracy and reduce the time for analysis and detection. Accordingly, for the detection method, we recommend using the Random Forest supervised classification algorithm. The experimental results in section 4.1 have proven that our proposal that use the Random Forest algorithm for abnormal behavior detection is completely correct because the results of this algorithm are much better than some other detection algorithms on all measures. For the feature optimization solution, we propose to use some data dimensional reduction techniques such as information gain, principal component analysis, and correlation coefficient method. The results of the research proposed in our paper have proven that to optimize the cyber-attack detection process, it is not necessary to use advanced algorithms with complex and cumbersome computational requirements, it must depend on the monitoring data for selecting the reasonable feature extraction and optimization algorithm as well as the appropriate attack classification and detection algorithms.

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“…In their experimental section, the research team [11] used these algorithms in turn to classify different cyber-attack components in the UNSW-NB15 dataset. In the study [12], the authors proposed a model to detect cyber-attacks using stacking techniques. Accordingly, in the training process of their model, the author uses machine learning algorithms consisting of K-Nearest Neighbors (KNN), Decision Tree (DT), and Logistic Regression (LR) in order to build a model based on the UNSW-NB15 and UGR'16 datasets.…”

Section: Related Workmentioning

confidence: 99%

Network anomaly detection technique based on LSTM network and UNSW-NB15 dataset

Duong¹

2020

IJATCSE

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Cyber-attack is a dangerous attack technique used by attackers to attack information systems. There are two methods to detect cyber-attacks that are based on the sign set and based on abnormal behavior of the network data. In particular, the detection method based on abnormal behavior analysis techniques is being highly effective due to strong changes in technologies and algorithms in machine learning and deep learning. In this paper, we propose a method for detecting anomalies in networks using the Long Short Term Memory (LSTM) network and the UNSW-NB15 dataset Accordingly, based on the UNSW-NB15 dataset that was analyzed and extracted features of the network traffic, we use the LSTM network to classify into abnormal behavior or normal behavior. To see the effectiveness of the LSTM network in the problem of classifying and evaluating network behavior, in this paper, we will change the structure and parameters of the algorithm. These experimental results are presented in the experimental section of the paper.

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A predictive model for network intrusion detection using stacking approach

Cited by 9 publications

References 22 publications

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Optimization of network traffic anomaly detection using machine learning

Network anomaly detection technique based on LSTM network and UNSW-NB15 dataset

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