REVEAL: Real-time Evaluation and Verification of External Adversarial Links
Department of Defense (DOD) use of commercial networks entails unprecedented reliance on untrusted third-party communications infrastructure, and the associated risk of exposing DOD communications to an adversary. Traversing adversary-controlled infrastructure allows DOD’s adversaries to recognize, disrupt, or extract intelligence even from encrypted communications. The resulting arms race of obfuscation vs intelligence techniques has an inherent limitation: with each new obfuscation, DOD can never know if it fools the adversary, or if the adversary is simply lulling DOD into a false sense of security.We believe the next great capability leap for operating through commercial networks will likely come from sophisticated analytics that provide situational awareness of the threats within the communications infrastructure, and implementations that dynamically route communications along benign paths. These systems will restructure communication paths to avoid adversary-controlled infrastructure within the cellular networks and the broader Internet, adding depth to existing DOD defenses and keeping communications unobservable by the adversary.In this paper, we describe our vision for this emerging conceptual framework. We first describe the threat model for DOD communications and use cases that motivate our approach. We then discuss specific threats for each component of the cellular communication infrastructure–—radio access network, mobile core, and Internet–—and our vision for securing communications against those threats. For the radio access network and Internet components, we also describe proofs-of-concept for our proposed approaches, demonstrating their feasibility.
Authors
- Matthew Luckie (ORCID: https://orcid.org/0000-0002-3872-4624)
- Bradley Huffaker (ORCID: https://orcid.org/0009-0009-3468-1266)
- Lincoln Thurlow
- Erik Kline (ORCID: https://orcid.org/0000-0003-0020-0056)
- Kyle Jamieson (ORCID: https://orcid.org/0000-0002-7940-2867)
- Alexander Marder (ORCID: https://orcid.org/0000-0003-3327-1571)
- kc claffy (ORCID: https://orcid.org/0000-0003-4824-3493)
- Jon Larrea
Institutions
- Johns Hopkins University (US)
- Princeton University (US)
- Integrated Systems Incorporation (United States) (US)
Publication Details
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1109/milcom61039.2024.10773880
- Citations
- 1
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.35