Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset

Kurniabudi, Kurniabudi; Stiawan, Deris; Darmawijoyo, Darmawijoyo; Idris, Mohd. Yazid; Kerim, Bedine; Budiarto, Rahmat

doi:10.52549/ijeei.v9i2.3028

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…These metrics are commonly represented between 0.00 to 1.00 or in percentage. Despite the differences between models being small, they are still considered important in deciding the best model, as demonstrated in [46], [47]. Besides using the CV score, the LGBM model is also evaluated by using Shapley Additive Explanation (SHAP) value to see the influence of each feature [48].…”

Section: Methodsmentioning

confidence: 99%

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Sengkey

Masengi

2023

IJEEI

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Numerous approaches have been developed to group learners' behavior in an online/blended learning environment. However, most clustering analyses in this particular field only consider numeric features despite the existence of categoric features that are found important in other studies. In this study, we compare K-Means and K-Prototypes algorithms to cluster learners' behavior in a flipped classroom implementation. From the model selection, we found that the model produced by the K-Prototypes algorithmwhich included categoric featuresis a better one. The statistical analysis of the clustering results of the selected K-Prototypes model shows significant differences in most of the inter-cluster comparisons, implying a good separation of the data. More importantly, we can identify the behavior in each cluster which then can be used to help learners in achieving better results in learning.

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Section: Methodsmentioning

confidence: 99%

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Sengkey

Masengi

2023

IJEEI

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“…Table 2 lists the features that have a score for them. PacketLengthVariance 0.532 [26], [28], [29], [32], [33] 8 AveragePacketSize 0.81 [25], [26], [28], [29], [33], [34]…”

Section: Data Pre-processing and Feature Selectionmentioning

confidence: 99%

Fast and accurate classifying model for denial-of-service attacks by using machine learning

Kareem

Jasim

2022

Bulletin EEI

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A denial of service (DoS) attack is one of the dangerous threats to networks that Internet resources and services will be less available, as they are easily operated and difficult to detect. As a result, identifying these intrusions is a hot issue in cybersecurity. Intrusion detection systems that use classic machine learning algorithms have a long testing period and high computational complexity. Therefore, it is critical to develop or improve techniques for detecting such an attack as quickly as possible to reduce the impact of the attack. As a result, we evaluate the effectiveness of rapid machine learning methods for model testing and generation in communication networks to identify denial of service attacks. In WEKA tools, the CICIDS2017 dataset is used to train and test multiple machine learning algorithms. The wide learning system and its expansions and the REP tree (REPT), random tree (RT), random forest (RF), decision stump (DS), and J48 were all evaluated. Experiments have shown that J48 takes less testing time and performs better, whereases it is performed by using 4-8 features. An accuracy result of 99.51% and 99.96% was achieved using 4 and 8 features, respectively.

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“…The complexity of the dataset is another challenge in the NIDS model development. To reduce computational resources and processing time, the researchers experimented with various algorithms to select and reduce features in response to the large volume and quantity of data [15].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Network Intrusion Detection System Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

Jihado,

Girsang

2024

JAIT

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Network security has become crucial in an era where information and data are valuable assets. An effective Network Intrusion Detection System (NIDS) is required to protect sensitive data and information from cyberattacks. Numerous studies have created NIDS using machine learning algorithms and network datasets that do not accurately reflect actual network data flows. Increasing hardware capabilities and the ability to process big data have made deep learning the preferred method for developing NIDS. This study develops a NIDS model using two deep learning algorithms: Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BiLSTM). CNN extracts spatial features in the proposed model, while BiLSTM extracts temporal features. Two publicly available benchmark datasets, CICIDS2017 and UNSW-NB15, are used to evaluate the model. The proposed model surpasses the previous method in terms of accuracy, achieving 99.83% and 99.81% for binary and multiclass classification on the CICIDS2017 dataset. On the UNSW-NB15 dataset, the model achieves accuracies of 94.22% and 82.91% for binary and multiclass classification, respectively. Moreover, Principal Component Analysis (PCA) is also used for feature engineering to improve the speed of model training and reduce existing features to ten dimensions without significantly impacting the model's performance.

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Important Features of CICIDS-2017 Dataset For Anomaly Detection in High Dimension and Imbalanced Class Dataset

Cited by 7 publications

References 25 publications

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Mixed-type Variables Clustering for Learners’ Behavior in Flipped Classroom Implementation

Fast and accurate classifying model for denial-of-service attacks by using machine learning

Hybrid Deep Learning Network Intrusion Detection System Based on Convolutional Neural Network and Bidirectional Long Short-Term Memory

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