Unveiling DoH tunnel: Toward generating a balanced DoH encrypted traffic dataset and profiling malicious behavior using inherently interpretable machine learning

Niktabe, Sepideh; Lashkari, Arash Habibi; Roudsari, Arousha Haghighian

doi:10.1007/s12083-023-01597-4

Cited by 2 publications

References 108 publications

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Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

Lin,

Sung,

Park

2024

Systems

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This study aims to introduce a novel approach for predicting China’s consumer confidence index (CCI), a key economic indicator that reflects consumers’ confidence in current and future economic conditions. While traditional statistical models and economic indicators are the primary tools for forecasting CCI, their reliance on linear assumptions limits their ability to capture the complex, dynamic relationships inherent in economic systems. In response, this study proposes a two-step method that integrates social network analysis (SNA) and machine learning (ML) to enhance prediction accuracy by accounting for the nonlinear interactions and systemic interdependencies that drive consumer confidence. The use of SNA enables the identification of critical variables and their interconnected roles in shaping consumer sentiment, while ML models, specifically the gradient boosting decision tree (GBDT), leverage these relationships to provide more precise predictions. Utilizing monthly data from 1999 to 2023, the combined SNA and GBDT approach significantly improves the accuracy of CCI forecasts, particularly during periods of high volatility. The results of this study hold substantial value for policymakers, market analysts, and economists, as they offer a systems-oriented framework for economic forecasting. By demonstrating the effectiveness of combining SNA with ML technologies, this research not only advances the methodological toolkit for economic forecasting, but also provides a new lens through which the complex, adaptive nature of economic systems can be better understood and managed. This integrated approach paves the way for future developments in forecasting models that more accurately reflect the evolving dynamics of consumer confidence in a rapidly changing economic environment.

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Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

Lin,

Sung,

Park

2024

Systems

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DoH Deception: Evading ML-Based Tunnel Detection Models with Real-world Adversarial Examples

Valente,

Osti,

P. Júnior

et al. 2024

Anais Do XXIV Simpósio Brasileiro De Segurança Da Informação E De Sistemas Computacionais (SBSeg 2024)

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Previous research on DNS over HTTPS (DoH) tunnel detection has focused on developing detection Machine Learning (ML) models, emphasizing accuracy and explainability. However, these models have neglected the threat of adversarial attacks, rendering them vulnerable and less robust. Our study reveals that most state-of-the-art DoH tunnel detection models are likely susceptible to adversarial black-box attacks. We adopt a novel approach by adapting the Zeroth Order Optimization (ZOO) attack to support DoH request features. The most constrained adaptation generated adversarial examples for 5 out of 6 DoH public tunnel tools. Our methods have successfully evaded the four most used state-of-the-art tunnel detection architectures. The technique relies on network flows and does not depend on the DoH request format. Thus, researchers can use it to create more robust DoH tunnel classifiers that target similar architectures in different security domains.

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Unveiling DoH tunnel: Toward generating a balanced DoH encrypted traffic dataset and profiling malicious behavior using inherently interpretable machine learning

Cited by 2 publications

References 108 publications

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

Integrated Systematic Framework for Forecasting China’s Consumer Confidence: A Machine Learning Approach

DoH Deception: Evading ML-Based Tunnel Detection Models with Real-world Adversarial Examples

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