A Policy based Framework for Privacy-Respecting Deep Packet Inspection of High Velocity Network Traffic
| dc.contributor.author | Renjan, Arya | |
| dc.contributor.author | Narayanan, Sandeep Nair | |
| dc.contributor.author | Joshi, Karuna Pande | |
| dc.date.accessioned | 2019-10-01T14:11:20Z | |
| dc.date.available | 2019-10-01T14:11:20Z | |
| dc.date.issued | 2019-05 | |
| dc.description | IEEE International Conference on Big Data Security on Cloud, May 2019. | en_US |
| dc.description.abstract | Deep Packet Inspection (DPI) is instrumental in investigating the presence of malicious activity in network traffic, and most existing DPI tools work on unencrypted payloads. As the internet is moving towards fully encrypted data-transfer, there is a critical requirement for privacy-aware techniques to efficiently decrypt network payloads. Until recently, passive proxying using certain aspects of TLS 1.2 were used to perform decryption and further DPI analysis. With the introduction of TLS 1.3 standard that only supports protocols with Perfect Forward Secrecy (PFS), many such techniques will become ineffective. Several security solutions will be forced to adopt active proxying that will become a big-data problem considering the velocity and veracity of network traffic involved. We have developed an ABAC (Attribute Based Access Control) framework that efficiently supports existing DPI tools while respecting user’s privacy requirements and organizational policies. It gives the user the ability to accept or decline access decision based on his privileges. Our solution evaluates various observed and derived attributes of network connections against user access privileges using policies described with semantic technologies. In this paper, we describe our framework and demonstrate the efficacy of our technique with the help of use-case scenarios to identify network connections that are candidates for Deep Packet Inspection. Since our technique makes selective identification of connections based on policies, both processing and memory load at the gateway will be reduced significantly | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/8818977 | en_US |
| dc.format.extent | 6 pages | en_US |
| dc.genre | conference papers and proceedings postprints | en_US |
| dc.identifier | doi:10.13016/m27qy4-ju90 | |
| dc.identifier.citation | A. Renjan, S. N. Narayanan and K. P. Joshi, "A Policy Based Framework for Privacy-Respecting Deep Packet Inspection of High Velocity Network Traffic," 2019 IEEE 5th Intl Conference on Big Data Security on Cloud (BigDataSecurity), IEEE Intl Conference on High Performance and Smart Computing, (HPSC) and IEEE Intl Conference on Intelligent Data and Security (IDS), Washington, DC, USA, 2019, pp. 47-52, doi: 10.1109/BigDataSecurity-HPSC-IDS.2019.00020. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/14953 | |
| dc.identifier.uri | 10.1109/BigDataSecurity-HPSC-IDS.2019.00020 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.rights | ©2019 IEEE | |
| dc.subject | Attribute-based Access Control (ABAC) | en_US |
| dc.subject | Deep Packet Inspection | en_US |
| dc.subject | TLS 1.3 | en_US |
| dc.subject | Perfect Forward Secrecy | en_US |
| dc.subject | Semantic Technologies | en_US |
| dc.subject | Privacy | en_US |
| dc.subject | UMBC Ebiquity Research Group | |
| dc.title | A Policy based Framework for Privacy-Respecting Deep Packet Inspection of High Velocity Network Traffic | en_US |
| dc.type | Text | en_US |