| NSF Org: |
CNS Division Of Computer and Network Systems |
| Recipient: |
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| Initial Amendment Date: | July 25, 2016 |
| Latest Amendment Date: | April 10, 2017 |
| Award Number: | 1566388 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Nina Amla
namla@nsf.gov (703)292-7991 CNS Division Of Computer and Network Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | August 1, 2016 |
| End Date: | July 31, 2019 (Estimated) |
| Total Intended Award Amount: | $174,739.00 |
| Total Awarded Amount to Date: | $182,739.00 |
| Funds Obligated to Date: |
FY 2017 = $8,000.00 |
| History of Investigator: |
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| Recipient Sponsored Research Office: |
2200 VINE ST # 830861 LINCOLN NE US 68503-2427 (402)472-3171 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
2200 Vine St, 151 Whittier Lincoln NE US 68503-1435 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): |
CRII CISE Research Initiation, Special Projects - CNS |
| Primary Program Source: |
01001718DB NSF RESEARCH & RELATED ACTIVIT |
| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
The development of the mobile Internet economy has brought numerous benefits to people and society, with the promise of providing ubiquitous computing and communications. Mobile devices have penetrated almost every aspect of our lives and, as a result, are storing a large amount of personal data. Unfortunately, the promise of the mobile Internet is easily undermined by "smart" malware and botnets, creating a precarious situation in which sensitive data stored on mobile devices could be leaked to adversaries through the mobile Internet or a wealth of compromised mobile devices could launch a denial of service attack to destruct the mobile infrastructure. This project develops non-intrusive, network-based solutions to detect mobile malware and botnets and mitigate their impact to ensure that mobile communications are carried out in a trustworthy manner despite the potential security threats. The research offers valuable insights into mobile malware's spreading mechanisms and malicious intents and will inspire studies in network behavior analysis of mobile applications. The project also has an important educational impact via the creation of new mobile security course projects and modules, widening students' views of mobile system security, and guiding next-generation mobile developers to include security and privacy considerations in designing mobile protocols and apps.
This project addresses three closely intertwined research issues in developing a network-based mobile malware detection system. The first part focuses on investigating malware traffic collection by identifying malware's network-related application program interfaces (APIs) and designing novel inputs to activate the malware's covert network behaviors. The second part focuses on designing a network-based malware detection system that identifies potential malware features based on their malicious network behaviors, which in turn will provide precise and unique identification of mobile malware. The third part focuses on the development of group behavior based detection mechanisms to identify organized network activities from malicious botnets that are built on the cooperation of malware. A local testbed will be developed to evaluate the performance of the proposed techniques and system designs, which aims to guarantee that the technologies developed are suitable for deployment in real mobile systems. The project uses machine learning techniques, statistical tools, and network traffic analysis to support secure communications in mobile networks.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The development of the mobile Internet economy has brought numerous benefits to people and society, with the promise of providing ubiquitous computing and communications. Unfortunately, the promise of the mobile Internet is easily undermined by "smart" malware and botnets, creating a precarious situation in which sensitive data stored on mobile devices could be leaked to adversaries through the mobile Internet or a wealth of compromised mobile devices could launch a denial of service attack to destruct the mobile infrastructure. This project addresses the challenges of designing systems to detect mobile malware's malicious network and system behaviors. This project enables research to exploit the recent advances in program analysis, machine learning, malware analysis, reverse engineering techniques to accurately and efficiently identify mobile malware. The outcomes of this research have advanced the state-of-the-art of mobile malware analysis in the following aspects.
First, this research creates a behavioral monitoring framework to systematically activate mobile malware and study the network behaviors of mobile apps both from the system-level and network-level perspectives. The new framework and generated datasets could benefit the mobile security research community. Second, this research investigates the mobile malware's malicious network activities using data analytics, and proposes multiple efficient systems to detect mobile malware using novel machine learning methods including the recent applications of deep neural networks. Third, this research systematically studies the vulnerable interactive behaviors of mobile apps, and discovers new vulnerabilities in the apps and identifies their potential threats. Multiple detection systems are developed to address the challenge of detecting zero-day vulnerabilities, unseen malware variants, and obfuscated malware samples.
The project makes an important educational impact via the creation of new mobile security course projects and modules, widening students' views of mobile system security, and guiding next-generation mobile developers to include security and privacy considerations in designing mobile protocols and apps. This project has contributed to the education and training of graduate and undergraduate students. It provides opportunities for graduate students to participate in the research, and will encourage the students to pursue a future career in research and development in the field of cyber security.
Last Modified: 11/21/2019
Modified by: Qiben Yan
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