Abstract
Cybersecurity is crucial in mitigating illegal access and hacking of online resources and network systems, and with the protection of network traffic vital by various detection and prevention techniques against cyber-attacks. Internet communication that occurs on the Dark Web in particular, contains sensitive data that should remain inaccessible to unauthorized users. However, threat actors are empowered by anonymous browsing on the Dark Web. This paper investigates browser fingerprint attacks on the Dark Web, focusing on identifying attack patterns. A detection model based on traditional machine learning techniques is developed using a large sample of fingerprint data. Feature selection techniques are applied to create optimized training and test datasets for classification. The model is validated using cross-validation methods to minimize overfitting and underfitting. Empirical results demonstrate that the proposed model effectively identifies malicious browsers on the Dark Web.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024 |
| Editors | Adel Al-Jumaily, Md Rafiqul Islam, Syed Mohammad Shamsul Islam, Md Rezaul Bashar |
| Publisher | IEEE Computer Society Press |
| Pages | 100-105 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798350391213 |
| ISBN (Print) | 9798350391213 (Print on demand) |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024 - Western Sydney University, Sydney, Australia Duration: 20 Nov 2024 → 23 Nov 2024 https://web.archive.org/web/20241123112455/https://www.fmlds.org/ (Conference website) https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10874081 (Front matter) https://ieeexplore.ieee.org/xpl/conhome/10873862/proceeding (Proceedings) |
Publication series
| Name | Proceedings - 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024 |
|---|
Conference
| Conference | 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024 |
|---|---|
| Country/Territory | Australia |
| City | Sydney |
| Period | 20/11/24 → 23/11/24 |
| Other | The Conference on Future Machine Learning and Data Science, Sydney, 2024 (FMLDS-2024) will bring international and national experts in Artificial Intelligence, Computer Vision, Future Machine Learning Technologies and Data Science. The conference will be jointly organised by Global Circle for Scientific, Technological and Management Research (GCSTMR) as its 7th World Congress and IEEE NSW Section. GCSTMR endeavours to create a platform for young researchers to exchange ideas and share information with experts in their respective fields. The conference will be held in a hybrid format, both physical and virtual. The physical venue will take place at Western Sydney University’s Penrith campus, well connected by rail and road to Sydney CBD. Prominent landmarks such as Sydney Harbour, Sydney Opera House and Harbour Bridge, along with the Blue Mountains World Heritage National Park, are easily accessible from the conference venue. The virtual sessions will be conducted via Zoom. FMLDS-2024 will feature keynote addresses by world leaders in Machine Learning and Data Science both from industry and academia. It will cover a wide range of areas within Artificial Intelligence (AI), Computer Vision, Future Machine Learning Technologies, Pattern Recognition, Motion Tracking, Cyber Security, Bioinformatics, Internet of Things and Data Science research where leading researchers as well as new researchers and computer practitioners will be able to exchange their views and ideas. All the papers submitted to FMLDS-2024 will be reviewed by at least two independent reviewers and if accepted will be published in the IEEE Xplore Digital Library. |
| Internet address |
|