Darknet Traffic Classification with Machine Learning Algorithms and SMOTE Method

Karagol, Hasan; Erdem, Oğuzhan; Akbas, Barkin; Soylu, Tuncay

doi:10.1109/ubmk55850.2022.9919462

Cited by 4 publications

(4 citation statements)

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“…Traffic attacks Studies on new proposed traffic attacks [46], [37], [47] Relay detection Deanonymizing Tor traffic through relay detection [48], [49] Counterattack Counter attack techniques in the dark web [50], [51], [52] Real-time attack detection Applying attack detection techniques on real-time data [53], [54], [55] Data balancing Impact of data balancing Studies focusing on data balancing techniques and their impact on darknet traffic classification [56], [57] Feature selection Feature selection algorithm Studies focusing on the feature selection for the classification purpose [58], [57], [12], [59] Figure 9 Percentage Classification of the Literature reviewed…”

Section: Classification Of Applicationsmentioning

confidence: 99%

“…This process aims to address the issue of imbalanced class set sizes within the traffic datasets, hence mitigating any classification biases. The SMOTE technique, as described in reference [56], generates a balanced dataset by artificially augmenting the number of samples belonging to the minority class in an imbalanced dataset. This methodology is commonly employed in areas characterized by limited accessibility or high costs associated with data acquisition, particularly in domains such as healthcare and internet traffic analysis.…”

Section: Feature Selection (Rq22)mentioning

confidence: 99%

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Darknet Traffic Analysis: A Systematic Literature Review

Saleem,

Islam,

Islam

2024

IEEE Access

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The primary objective of an anonymity tool is to protect the anonymity of its users through the implementation of strong encryption and obfuscation techniques. As a result, it becomes very difficult to monitor and identify users' activities on these networks. Moreover, such systems have strong defensive mechanisms to protect users against potential risks, including the extraction of traffic characteristics and website fingerprinting. However, the strong anonymity feature also functions as a refuge for those involved in illicit activities who aim to avoid being traced on the network. As a result, a substantial body of research has been undertaken to examine and classify encrypted traffic using machine-learning techniques. This paper presents a comprehensive examination of the existing approaches utilized for the categorization of anonymous traffic as well as encrypted network traffic inside the darknet. Also, this paper presents a comprehensive analysis of methods of darknet traffic using ML (machine learning) techniques to monitor and identify the traffic attacks inside the darknet.

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Section: Classification Of Applicationsmentioning

confidence: 99%

Section: Feature Selection (Rq22)mentioning

confidence: 99%

Darknet Traffic Analysis: A Systematic Literature Review

Saleem,

Islam,

Islam

2024

IEEE Access

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“…The imbalance in class can be tackled using many approaches. The Synthetic Minority Over-sampling TEchnique (SMOTE)-based approach for improving the accuracy of the algorithms for imbalanced datasets has been discussed in the research by Chawla et al (2002), Bahaweres et al (2022), Pribadi et al (2022), andKaragöl et al (2022).…”

Section: Approaches For Handling An Imbalanced Datasetmentioning

confidence: 99%

“…Using Twitter data regarding the Sinovac vaccine on an imbalance class, Pribadi et al (2022) conducted a sentiment analysis and discovered that, after SMOTE optimization, the accuracy values of the three methods they employed, on average, increased by 14%. Karagöl et al (2022) classified darknet traffic using six different machine learning methods, both with and without SMOTE, and found that the SMOTE approach produced the highest accuracy results.…”

Section: Approaches For Handling An Imbalanced Datasetmentioning

confidence: 99%

Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data

Saminathan¹,

Malathy²

2023

Front. Big Data

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Air pollution is a serious challenge to humankind as it poses many health threats. It can be measured using the air quality index (AQI). Air pollution is the result of contamination of both outdoor and indoor environments. The AQI is being monitored by various institutions globally. The measured air quality data are kept mostly for public use. Using the previously calculated AQI values, the future values of AQI can be predicted, or the class/category value of the numeric value can be obtained. This forecast can be performed with more accuracy using supervised machine learning methods. In this study, multiple machine-learning approaches were used to classify PM2.5 values. The values for the pollutant PM2.5 were classified into different groups using machine learning algorithms such as logistic regression, support vector machines, random forest, extreme gradient boosting, and their grid search equivalents, along with the deep learning method multilayer perceptron. After performing multiclass classification using these algorithms, the parameters accuracy and per-class accuracy were used to compare the methods. As the dataset used was imbalanced, a SMOTE-based approach for balancing the dataset was used. Compared to all other classifiers that use the original dataset, the accuracy of the random forest multiclass classifier with SMOTE-based dataset balancing was found to provide better accuracy.

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Enhancing Darknet Traffic Analysis through Top Ranked Feature Selection Methods

Bhapkar,

Kshirsagar

2023

2023 International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security (iQ-CC

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Darknet Traffic Classification with Machine Learning Algorithms and SMOTE Method

Cited by 4 publications

References 0 publications

Darknet Traffic Analysis: A Systematic Literature Review

Darknet Traffic Analysis: A Systematic Literature Review

Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data

Enhancing Darknet Traffic Analysis through Top Ranked Feature Selection Methods

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