Machine Learning-Based Classification of Encrypted Internet Traffic

Tabatabaei, Talieh Seyed; Adel, Mostafa; Karray, Fakhri; Kamel, Mohamed S.

doi:10.1007/978-3-642-31537-4_45

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…The efficiency of the classifier has been calculated according to the confusion matrix (CM) as shown in Figure3. For each record in the testing dataset the following evaluation metric is applied: • False-Positive CM[0] [1]: the number of non-VPN packets that are incorrectly classified as VPN.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…Traffic Classification is the principle of recognition of protocols and implementations by evaluating the network traffic. Traffic classification techniques are used for a wide variety of purposes, including Quality of Service (QoS), traffic forming, Intrusion Detection Systems (IDS), and network forensic solutions [1,2,3,4,5,6]. Usually, traffic can be classified as normal or malware traffic to detect and prevent attacks.…”

Section: Introductionmentioning

confidence: 99%

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VPN Encrypted Traffic classification using XGBoost

2021

IJETER

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Classification network traffic are becoming ever more relevant in understanding and addressing security issues inInternet applications. Virtual Private Networks (VPNs) have become one famous communication forms on the Internet. In this study, a new model for traffic classification into VPN or non-VPN is proposed. XGBoost algorithm is used to rank features and to build the classification model. The proposed model overwhelmed other classification algorithms. The proposed model achieved 91.6% accuracy which is the highest registered accuracy for the selected dataset. To illustrate the merit of the proposed model, a comparison was made with sixteen different classification algorithms

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Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VPN Encrypted Traffic classification using XGBoost

2021

IJETER

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“…The approach provides very accurate results in classifying applications [3] . Hence, it is mostly employed in commercial tools, however [4], DPI performs poorly against encrypted traffics, and incurs high computational overhead [5].…”

Section: Introductionmentioning

confidence: 99%

“…The main intuition behind such methods is that the statistical attributes of network traffic are unique for different applications and can therefore be used to differentiate applications from each other [6]. Machine learning (ML) methods on the other hand are highly efficient in dealing with statistical data [4]. Therefore, several machine learning algorithms such as K-Nearest Neighbor [7], Random Forest [3] , Support Vector [8] Machines are employed.…”

Section: Introductionmentioning

confidence: 99%

A Review of Deep Learning Techniques for Encrypted Traffic Classification

Iliyasu¹,

Abba²,

Iliyasu³

et al. 2022

CIML

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Network traffic classification is significant for task such as Quality of Services (QoS) provisioning, resource usage planning, pricing as well as in the context of security such as in Intrusion detection systems. The field has received considerable attention in the industry as well as research communities where approaches such as Port based, Deep packet Inspection (DPI), and Classical machine learning techniques were thoroughly studied. However, the emergence of new applications and encryption protocols as a result of continuous transformation of Internet has led to the rise of new challenges. Recently, researchers have employed deep learning techniques in the domain of network traffic classification in order to leverage the inherent advantages offered by deep learning models such as the ability to capture complex pattern as well as automatic feature learning. This paper reviews deep learning based encrypted traffic classification techniques, as well as highlights the current research gap in the literature. Index Terms : Traffic classification, Encrypted traffic, Deep learning, Machine learning.

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