Text Classification of Illegal Activities on Onion Sites

Buldin, Ilya D.; Ivanov, Nikita S.

doi:10.1109/eiconrus49466.2020.9039341

Cited by 15 publications

(11 citation statements)

References 4 publications

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“…The similarity between two data points is measured by estimating distance, proximity or closeness function [43]. KNN classifier computes classification based on a simple majority vote of the nearest neighbors of each data point [34,44]. The number of nearest neighbors (K) is determined by specification or by estimating the number of neighbors within a fixed radius of each point.…”

Section: K-nearest Neighbormentioning

confidence: 99%

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COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

et al. 2020

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naïve Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

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Section: K-nearest Neighbormentioning

confidence: 99%

“…The number of nearest neighbors (K) is determined by specification or by estimating the number of neighbors within a fixed radius of each point. KNN classifiers are simple, easy to implement and applicable for multi-class problems [36,44,45].…”

Section: K-nearest Neighbormentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

et al. 2020

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“…The author claim that the impact of time-related features is higher than that of the non-time-related features on the mobile platform, while it is the opposite on the PC platform. The researcher in [28] proposed a machine learning approach to text classification over the Russian languagebased Tor sites. The proposed model analyzes the text and categorize it for the forensics purpose.For a quick analysis, we conclude state-of-the-art studies in Table 1.…”

Section: Related Workmentioning

confidence: 99%

DarkDetect: Darknet Traffic Detection and Categorization Using Modified Convolution-Long Short-Term Memory

et al. 2021

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Darknet is commonly known as the epicenter of illegal online activities. An analysis of darknet traffic is essential to monitor real-time applications and activities running over the Darknet. Recognizing network traffic bound to unused Internet addresses has become undeniably significant for identifying and examining malicious activities on the internet. Since there are no authentic hosts or devices in an unused address block, any observed network traffic must be the aftereffect of misconfiguration from spoofed source addressed and other frameworks that monitor unused address space. However, the recent advancements in artificial intelligence allow digital systems to detect and identify darknet traffic autonomously. In this paper, we propose a generalized approach for darknet traffic detection and categorization using Deep Learning. We examine the state-of-the-art complex dataset, which provides excessive information about the darknet traffic and perform data preprocessing. Next, we analyze diverse feature selection techniques to select optimal features for darknet traffic detection and categorization. We apply fine-tuned machine learning (ML) algorithms which include Decision Tree (DT), Gradient Boosting (GB), Random Forest Regressor (RFR), and Extreme Gradient Boosting (XGB) on selected features and compare the performance. Next, we apply modified Convolution-Long Short-Term Memory (CNN-LSTM) and Convolution-Gradient Recurrent Unit (CNN-GRU) deep learning techniques to recognize the network traffic more accurately. The results demonstrate that the proposed approach outperforms the existing approaches by yielding the maximum accuracy of 96% of darknet traffic detection and 89% of darknet traffic categorization through XGB as a feature selection approach and CNN-LSTM a recognition model.

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“…The similarity between two data points is measured by estimating distance, proximity or closeness function [46]. KNN classifier computes classification based on a simple majority vote of the nearest neighbors of each data point [37,47]. The number of nearest neighbors (K) is determined by specification or by estimating the number of neighbors within a fixed radius of each point.…”

Section: Logistic Regressionmentioning

confidence: 99%

Section: Logistic Regressionmentioning

confidence: 99%

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

Samuel

Ali

Rahman

et al. 2020

Preprint

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Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning (ML) classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91\% for short Tweets, with the Na\"ive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74\% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

show abstract

Text Classification of Illegal Activities on Onion Sites

Cited by 15 publications

References 4 publications

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

DarkDetect: Darknet Traffic Detection and Categorization Using Modified Convolution-Long Short-Term Memory

COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification

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