| Title | Machine learning for anonymous traffic detection and classification |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | K M, Akshobhya |
| Conference Name | 2021 11th International Conference on Cloud Computing, Data Science Engineering (Confluence) |
| Keywords | Classification algorithms, dark web, dark-web, feature extraction, Human Behavior, I2P, JonDonym, machine learning algorithms, pubcrawl, security, telecommunication traffic, Time series analysis, Tor, Traffic classification, Training |
| Abstract | Anonymity is one of the biggest concerns in web security and traffic management. Though web users are concerned about privacy and security various methods are being adopted in making the web more vulnerable. Browsing the web anonymously not only threatens the integrity but also questions the motive of such activity. It is important to classify the network traffic and prevent source and destination from hiding with each other unless it is for benign activity. The paper proposes various methods to classify the dark web at different levels or hierarchies. Various preprocessing techniques are proposed for feature selection and dimensionality reduction. Anon17 dataset is used for training and testing the model. Three levels of classification are proposed in the paper based on the network, traffic type, and application. |
| DOI | 10.1109/Confluence51648.2021.9377168 |
| Citation Key | k_m_machine_2021 |
Machine learning for anonymous traffic detection and classification