A Survey on Analyzing Encrypted Network Traffic of Mobile Devices
June 22, 2020 ยท The Cartographer ยท ๐ International Journal of Information Security
"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey on Analyzing Encrypted Network Traffic of Mobile Devices"
Evidence collected by the PWNC Scanner
Authors
Ashutosh Bhatiaa, Ankit AgrawalaAyush Bahugunaa, Kamlesh Tiwaria, K. Haribabua, Deepak Vishwakarmab
arXiv ID
2006.12352
Category
cs.CR: Cryptography & Security
Citations
22
Venue
International Journal of Information Security
Last Checked
2 days ago
Abstract
Over the years, use of smartphones has come to dominate several areas, improving our lives, offering us convenience, and reshaping our daily work circumstances. Beyond traditional use for communication, they are used for many peripheral tasks such as gaming, browsing, and shopping. A significant amount of traffic over the Internet belongs to the applications running over mobile devices. Applications encrypt their communication to ensure the privacy and security of the user's data. However, it has been found that the amount and nature of incoming and outgoing traffic to a mobile device could reveal a significant amount of information that can be used to identify the activities performed and to profile the user. To that end, researchers are trying to develop techniques to classify encrypted mobile traffic at different levels of granularity, with the objectives of performing mobile user profiling, network performance optimization, $etc.$ This paper proposes a framework to categorize the research works on analyzing encrypted network traffic related to mobile devices. After that, we provide an extensive review of state of the art based on the proposed framework.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Cryptography & Security
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
๐ป
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
๐ป
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
๐ป
Ghosted
How To Backdoor Federated Learning
R.I.P.
๐ป
Ghosted