A novel dataset for encrypted virtual private network traffic analysis

Naas, Mohamed; Fesl, Jan

doi:10.1016/j.dib.2023.108945

Cited by 7 publications

(10 citation statements)

References 5 publications

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“…Packet size, flow duration, inter-arrival time, and protocol dispersion are a few typical features [3]. Entropybased characteristics [9] and features obtained from encrypted payloads using statistical analysis have both been investigated in recent publications [7].…”

Section: Machine Learning-based Vpn Classificationmentioning

confidence: 99%

“…Without labeled data, unsupervised approaches like clustering have also been used to categorize VPN traffic [28]. To further enhance classification performance and minimize annotation work, semi-supervised techniques that make use of both labeled and unlabeled data have been proposed [7].…”

Section: Machine Learning-based Vpn Classificationmentioning

confidence: 99%

“…It might be essential to regularly update the feature set and retrain the models in order to maintain accuracy of classification [2]. Furthermore, the performance of machine learning models can be adversely affected, if there is encrypted traffic present on a network, because it restricts the features that can be used to classify data [7]. Finally, privacy concerns may arise when handling network traffic data, particularly when dealing with payload content [3].…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

“…Recent advancements in machine learning for VPN traffic classification, as explored by Sikha Bagui [6,7] and Shane Miller [8], have demonstrated significant progress in distinguishing between VPN and non-VPN traffic using time-related features and flow statistics data, respectively. Bagui's work, in particular, highlighted the efficacy of using logistic regression, support vector machine, Naïve Bayes, k-nearest neighbor, and ensemble methods (Random Forest and Gradient Boosting Tree classifiers) for traffic classification, identifying features that contribute to achieving high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Integrating Blockchain and Deep Learning for Enhanced Mobile VPN Forensics: A Comprehensive Framework

Alqahtany,

Syed

2024

Applied Sciences

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In an era marked by technological advancement, the rising reliance on Virtual Private Networks (VPNs) necessitates sophisticated forensic analysis techniques to investigate VPN traffic, especially in mobile environments. This research introduces an innovative approach utilizing Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs) for classifying VPN traffic, aiding forensic investigators in precisely identifying applications or websites accessed via VPN connections. By leveraging the combined strengths of CNNs and GNNs, our method provides an effective solution for discerning user activities during VPN sessions. Further extending this framework, we incorporate blockchain technology to meticulously record all mobile VPN transactions, ensuring a tamper-proof and transparent ledger that significantly bolsters the integrity and admissibility of forensic evidence in legal scenarios. A specific use-case demonstrates this methodology in mobile forensics, where our integrated approach not only accurately classifies data traffic but also securely logs transactional details on the blockchain, offering an unprecedented level of detail and reliability in forensic investigations. Extensive real-world VPN dataset experiments validate our approach, highlighting its potential to achieve high accuracy and offering invaluable insights for both technological and legal domains in the context of mobile VPN usage.

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Section: Machine Learning-based Vpn Classificationmentioning

confidence: 99%

Section: Machine Learning-based Vpn Classificationmentioning

confidence: 99%

Section: Challenges and Future Directionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Integrating Blockchain and Deep Learning for Enhanced Mobile VPN Forensics: A Comprehensive Framework

Alqahtany,

Syed

2024

Applied Sciences

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“…D2C lead marketing is an emerging direct-to-consumer marketing strategy, which uses big data to analyze consumer behavior, accurately identify potential consumer needs, and provide personalized recommendations [4]. Its precision and efficiency help brands accurately reach target consumers and improve marketing effectiveness and conversion rates [5].…”

Section: Concept and Characteristics Of D2c Lead Marketingmentioning

confidence: 99%

Research on Private Domain Traffic Operation Link Based on D2C Lead Marketing

Cao

2024

FBEM

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This project aims to build an online platform, open up an integrated link integrating brand building, mental planting and effect transformation, obtain user leads through various marketing methods such as content creation and channel delivery, and establish a social customer relationship management system for the third-party automotive industry based on artificial intelligence technology. To carry out the link operation after retention collection, data cleaning, hierarchical screening, AI intelligent customer service, etc., to establish high-quality brand user clues database information. On the basis of exploring the realization of live streaming clues, this project will cooperate with the media operation room to build the mini program platform for users of Zhongqi Media, complete the introduction of the platform for prospective users of online and offline projects of Zhongqi Media, and conduct user operation through the modular construction of the platform. Build a complete private domain operation system of "public domain drainage, content planting, attracting and retaining funds, label classification, private domain precipitation, guided purchase, activation, potential customer tracking, and data duplication".

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Cybersecurity in Politics

Marwala

2023

Artificial Intelligence, Game Theory and Mechanism Design in Politics

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A novel dataset for encrypted virtual private network traffic analysis

Cited by 7 publications

References 5 publications

Integrating Blockchain and Deep Learning for Enhanced Mobile VPN Forensics: A Comprehensive Framework

Integrating Blockchain and Deep Learning for Enhanced Mobile VPN Forensics: A Comprehensive Framework

Research on Private Domain Traffic Operation Link Based on D2C Lead Marketing

Cybersecurity in Politics

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