A Survey on Feature Selection Techniques for Internet Traffic Classification

Dhote, Yogesh; Agrawal, Shikha; Deen, Anjana Jayant

doi:10.1109/cicn.2015.267

Cited by 60 publications

(36 citation statements)

References 28 publications

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“…Various machine-learning methods had been combined to build hybrid models [46][47][48] in progressive or parallel structures [49], but most of the models were too complex for real-time classification. For the general statistical feature methods, Dorfinger et al [50] identified encrypted traffic according to the entropy of packet data; however, some studies [1,29] have recently found that the entropy method cannot distinguish between encrypted traffic and compressed traffic. Among them, KNN [51][52][53] is light and accurate, and could train a high-performance model with a small amount of labeled data.…”

Section: Related Workmentioning

confidence: 99%

“…Furthermore, trainFlows represents the flow features of the training samples after data normalization, while enTrainFlows represents the encrypted flow data of trainFlows. The sets of FI 1 , FI 2 and FI 3 are the selected feature index, while w (1) , w (2) and w (3) are the feature weight sets after training.…”

Section: Algorithm 3: Fce-knnmentioning

confidence: 99%

“…Traffic-classification technology plays an important role in network security defense mechanisms. It is the basis for analyzing network traffic, detecting network anomalies, and balancing network load [1]. However, while traffic encryption is often used to protect information transmission, it also complicates the network traffic classification and analysis [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Cao

2020

Electronics

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The fine-grained classification of encrypted traffic is important for network security analysis. Malicious attacks are usually encrypted and simulated as normal application or content traffic. Supervised machine learning methods are widely used for traffic classification and show good performances. However, they need a large amount of labeled data to train a model, while labeled data is hard to obtain. Aiming at solving this problem, this paper proposes a method to train a model based on the K-nearest neighbor (KNN) algorithm, which only needs a small amount of data. Due to the fact that the importance of different traffic features varies, and traditional KNN does not highlight the importance of different features, this study introduces the concept of feature weight and proposes the weighted feature KNN (WKNN) algorithm. Furthermore, to obtain the optimal feature set and the corresponding feature weight set, a feature selection and feature weight self-adaptive algorithm for WKNN is proposed. In addition, a three-layer classification framework for encrypted network flows is established. Based on the improved KNN and the framework, this study finally presents a method for fine-grained classification of encrypted network flows, which can identify the encryption status, application type and content type of encrypted network flows with high accuracies of 99.3%, 92.4%, and 97.0%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Algorithm 3: Fce-knnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Cao

2020

Electronics

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“…AND THE PROPOSED APPROACH 2.1 Filter approach A filter method selects a subset of features or ranks features based on some general characteristics of the features, independently without including any classification methods. [5][6][7][8][9][10][11] There are two main types of filter-based feature selection: unsupervised and supervised. Unsupervised methods select features according to distance or similarity/dissimilarity between features, [13,14] whilst supervised methods select features according to their correlation or relevance with class labels.…”

Section: Filter Approach Wrapper Approachmentioning

confidence: 99%

“…However, wrapper methods are usually more accurate than filter methods. [5][6][7][8][9][10][11] Embedded approach searches locally for features that allow better local discrimination. It uses independent criteria to decide on the optimal subsets for given cardinality, in which learning algorithms are usually used to select the final optimal feature subset.…”

Section: Introductionmentioning

confidence: 99%

Hybrid approaches to feature subset selection for data classification in high-dimensional feature space

Khalaf

Gan

2020

AIR

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This paper proposes two hybrid feature subset selection approaches based on the combination (union or intersection) of both supervised and unsupervised filter approaches before using a wrapper, aiming to obtain low-dimensional features with high accuracy and interpretability and low time consumption. Experiments with the proposed hybrid approaches have been conducted on seven high-dimensional feature datasets. The classifiers adopted are support vector machine (SVM), linear discriminant analysis (LDA), and K-nearest neighbour (KNN). Experimental results have demonstrated the advantages and usefulness of the proposed methods in feature subset selection in high-dimensional space in terms of the number of selected features and time spent to achieve the best classification accuracy.

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Feature Engineering for Flow‐Based IDS

Adhao¹,

Pachghare²

2022

Wireless Communication Security

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A Survey on Feature Selection Techniques for Internet Traffic Classification

Cited by 60 publications

References 28 publications

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Improved KNN Algorithm for Fine-Grained Classification of Encrypted Network Flow

Hybrid approaches to feature subset selection for data classification in high-dimensional feature space

Feature Engineering for Flow‐Based IDS

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