Fingerprinting Industrial IoT devices based on multi-branch neural network

Yang, Kai; Li, Qiang; Wang, Haining; Sun, Limin; Liu, Jiqiang

doi:10.1016/j.eswa.2023.122371

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Hierarchical classification: It is crucial to achieve different classification objectives in network functions such as QoS management, firewall, intrusion detection, and application banning. Hierarchical classification allows for dynamic granularity levels, eliminating the need for multiple classification modules and training multiple classifiers [ 99 ].…”

Section: Recommendations and Future Perspectivesmentioning

confidence: 99%

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Alwhbi,

Zou,

Alharbi

2024

Sensors

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Encryption is a fundamental security measure to safeguard data during transmission to ensure confidentiality while at the same time posing a great challenge for traditional packet and traffic inspection. In response to the proliferation of diverse network traffic patterns from Internet-of-Things devices, websites, and mobile applications, understanding and classifying encrypted traffic are crucial for network administrators, cybersecurity professionals, and policy enforcement entities. This paper presents a comprehensive survey of recent advancements in machine-learning-driven encrypted traffic analysis and classification. The primary goals of our survey are two-fold: First, we present the overall procedure and provide a detailed explanation of utilizing machine learning in analyzing and classifying encrypted network traffic. Second, we review state-of-the-art techniques and methodologies in traffic analysis. Our aim is to provide insights into current practices and future directions in encrypted traffic analysis and classification, especially machine-learning-based analysis.

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Section: Recommendations and Future Perspectivesmentioning

confidence: 99%

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Alwhbi,

Zou,

Alharbi

2024

Sensors

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Networked Industrial Control Device Asset Identification Method Based on Improved Decision Tree

Yang,

Fang,

Zhou

et al. 2024

J Netw Syst Manage

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Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning

Koziel,

Pietrenko-Dabrowska,

Ullah

2024

Sci Rep

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Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type.

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Fingerprinting Industrial IoT devices based on multi-branch neural network

Cited by 4 publications

References 15 publications

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Encrypted Network Traffic Analysis and Classification Utilizing Machine Learning

Networked Industrial Control Device Asset Identification Method Based on Improved Decision Tree

Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning

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