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Award Abstract # 1643207
EAGER: Collaborative: Towards Understanding the Attack Vector of Privacy Technologies

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: THE RESEARCH FOUNDATION FOR THE STATE UNIVERSITY OF NEW YORK
Initial Amendment Date: July 21, 2016
Latest Amendment Date: July 21, 2016
Award Number: 1643207
Award Instrument: Standard Grant
Program Manager: Phillip Regalia
pregalia@nsf.gov
 (703)292-2981
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: September 1, 2016
End Date: December 31, 2017 (Estimated)
Total Intended Award Amount: $125,000.00
Total Awarded Amount to Date: $125,000.00
Funds Obligated to Date: FY 2016 = $22,808.00
History of Investigator:
  • David Mohaisen (Principal Investigator)
    mohaisen@ucf.edu
Recipient Sponsored Research Office: SUNY at Buffalo
520 LEE ENTRANCE STE 211
AMHERST
NY  US  14228-2577
(716)645-2634
Sponsor Congressional District: 26
Primary Place of Performance: SUNY at Buffalo
White Rd
Buffalo
NY  US  14260-2500
Primary Place of Performance
Congressional District:
26
Unique Entity Identifier (UEI): LMCJKRFW5R81
Parent UEI: GMZUKXFDJMA9
NSF Program(s): Networking Technology and Syst,
Secure &Trustworthy Cyberspace
Primary Program Source: 01001617DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 7363, 7434, 7916
Program Element Code(s): 736300, 806000
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

Advances in privacy-enhancing technologies, including cryptographic mechanisms, standardized security protocols, and infrastructure, significantly improved privacy and had a significant impact on society by protecting users. At the same time, the success of such infrastructure has attracted abuse from illegal activities, including sophisticated botnets and ransomware, and has become a marketplace for drugs and contraband; botnets rose to be a major tool for cybercrime and their developers proved to be highly resourceful. It is contended that the next waves of botnets will extensively attempt to subvert privacy infrastructure and cryptographic mechanisms, which has the potential of both undermining their legal basis and future performance.

This project will develop the theoretical and experimental foundations for analyzing, monitoring and mitigating the next generation of botnets that subvert privacy-enhancing technologies. Towards that goal, the project will develop tools for: 1) Analytical framework: the project develops a concrete strategy for approaching the detection, characterization, and mitigation of abuse of privacy infrastructure by crystallizing an analytical framework for reasoning about such botnets. This includes the identification
and formalization of their key properties (e.g., traceback and tomography resiliency, stealthy monetization), enabling mechanisms (e.g., IP address de-coupling, control/data traffic indistinguishability), fundamental limitations, and evaluation metrics. The project will explore analogous scenarios of abuse in future Internet architectures where anonymity is facilitated by design. 2) Monitoring and analysis: the project develops an experimental framework to track activities of the next generation of botnets for scalable and effective mitigation. Such framework will exploit their ideal design and behavioral properties, and draws on various preliminary measurement results in related contexts. 3) Mitigation: The project has the ultimate
goal of proactively developing an arsenal of mitigation techniques grounded in a sound theoretical foundation, analyzed within the theoretical framework, and evaluated within the experimental framework. The mitigation techniques span the gamut of increasing the cost of operating such botnets, to actively containing
and neutralizing bots, to proposing modifications to the privacy-enhancing protocols. The results of this project will be communicated with the concerned communities for having a direct and immediate impact on existing and future privacy infrastructure. The project will also develop educational material to train students in the foundations and systems for enabling privacy enhancing technologies.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Chen, Si and Ren, Kui and Piao, Sixu and Wang, Cong and Wang, Qian and Weng, Jian and Su, Lu and Mohaisen, Aziz "You Can Hear But You Cannot Steal: Defending Against Voice Impersonation Attacks on Smartphones" Distributed Computing Systems (ICDCS), 2017 IEEE 37th International Conference on , 2017 10.1109/ICDCS.2017.133 Citation Details
Dang, Fan and Zhou, Pengfei and Li, Zhenhua and Zhai, Ennan and Mohaisen, Aziz and Wen, Qingfu and Li, Mo "Large-scale Invisible Attack on AFC Systems with NFC-equipped Smartphones" the 36th IEEE International Conf. on Computer Communications, INFOCOM 2017 , 2017 Citation Details
Mohaisen, Aziz and Gu, Zhongshu and Ren, Kui "Privacy Implications of DNSSEC Look-Aside Validation" 37th IEEE International Conference on Distributed Computing Systems, ICDCS 2017 , 2017 10.1109/ICDCS.2017.147 Citation Details
Mohaisen, Aziz and Ren, Kui "Leakage of .onion at the DNS Root: Measurements, Causes, and Countermeasures" IEEE/ACM Transactions on Networking , 2017 10.1109/TNET.2017.2717965 Citation Details
Wang, Qian and Ren, Kui and Du, Minxin and Li, Qi and and Mohaisen, Aziz "SecGDB: Graph Encryption for Exact Shortest Distance Queries with Efficient Updates." Financial Cryptography and Data Security, FC 2017 , 2017 Citation Details

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