2022
DOI: 10.3390/sym14102002
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Bidirectional Statistical Feature Extraction Based on Time Window for Tor Flow Classification

Abstract: The anonymous system Tor uses an asymmetric algorithm to protect the content of communications, allowing criminals to conceal their identities and hide their tracks. This malicious usage brings serious security threats to public security and social stability. Statistical analysis of traffic flows can effectively identify and classify Tor flow. However, few features can be extracted from Tor traffic, which have a weak representational ability, making it challenging to combat cybercrime in real-time effectively.… Show more

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Cited by 5 publications
(1 citation statement)
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“…This section examines the classification performance and effectiveness of four models. These models include the following: Lashkari et al [21] utilized time-based features extracted from 15-s Tor traffic to train a random forest model; Yan, H et al [29] employed sliding time windows to segment network traffic and calculated the relative entropy of traffic within the time window to identify Tor traffic; Haoyu Ma et al [30] proposed a deep-learning-based scheme for detecting dark web traffic (Tor traffic); and this paper presents a darknet traffic identification method based on autoencoder (AE-DTI). All of these models were trained on the ISCXTor dataset.…”
Section: Comparison Of Ae-dti With Other Methodsmentioning
confidence: 99%
“…This section examines the classification performance and effectiveness of four models. These models include the following: Lashkari et al [21] utilized time-based features extracted from 15-s Tor traffic to train a random forest model; Yan, H et al [29] employed sliding time windows to segment network traffic and calculated the relative entropy of traffic within the time window to identify Tor traffic; Haoyu Ma et al [30] proposed a deep-learning-based scheme for detecting dark web traffic (Tor traffic); and this paper presents a darknet traffic identification method based on autoencoder (AE-DTI). All of these models were trained on the ISCXTor dataset.…”
Section: Comparison Of Ae-dti With Other Methodsmentioning
confidence: 99%